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Simple Summary: In radiotherapy treatment, organs at risk and target volumes are contoured by the clinicians to prepare a dosimetry plan. In retrospective data, these structures are not often standardised to universal names across the patients plans, which is required to enable data mining and analysis. In this paper, a new method was proposed and evaluated to automatically standardise radiotherapy structures names using machine learning algorithms. The proposed approach was deployed over a dataset with 1613 patients collected from Liverpool & Macarthur Cancer Therapy Centres, New South Wales, Australia. It was concluded that machine learning techniques can standardise the dosimetry plan structures, taking into consideration the integration of multiple modalities representing each structure during the training process. In progressing the use of big data in health systems, standardised nomenclature is required to enable data pooling and analyses. In many radiotherapy planning systems and their data archives, target volumes (TV) and organ-at-risk (OAR) structure nomenclature has not been standardised. Machine learning (ML) has been utilised to standardise volumes nomenclature in retrospective datasets. However, only subsets of the structures have been targeted. Within this paper, we proposed a new approach for standardising all the structures nomenclature by using multi-modal artificial neural networks. A cohort consisting of 1613 breast cancer patients treated with radiotherapy was identified from Liverpool & Macarthur Cancer Therapy Centres, NSW, Australia. Four types of volume characteristics were generated to represent each target and OAR volume: textual features, geometric features, dosimetry features, and imaging data. Five datasets were created from the original cohort, the first four represented different subsets of volumes and the last one represented the whole list of volumes. For each dataset, 15 sets of combinations of features were generated to investigate the effect of using different characteristics on the standardisation performance. The best model reported 99.416% classification accuracy over the hold-out sample when used to standardise all the nomenclatures in a breast cancer radiotherapy plan into 21 classes. Our results showed that ML based automation methods can be used for standardising naming conventions in a radiotherapy plan taking into consideration the inclusion of multiple modalities to better represent each volume. [ABSTRACT FROM AUTHOR]

The popularity of video services such as video call or video on-demand has made it impossible for people to live without them in their daily lives. It can be anticipated that the explosive growth of vehicular communication owing to the widespread use of in-vehicle video infotainment applications in the future will result in increasing fragmentation and congestion of the wireless transmission spectrum. Accordingly, effective bandwidth management algorithms are demanded to achieve efficient communication and stable scalability in next-generation vehicular networks. To the best of our current knowledge, a noticeable gap remains in the existing literature regarding the application of the latest advancements in network communication technologies. Specifically, this gap is evident in the lack of exploration regarding how cutting-edge technologies can be effectively employed to optimize bandwidth allocation, especially in the realm of video service applications within the forthcoming vehicular networks. In light of this void, this paper presents a seamless integration of cutting-edge 6G communication technologies, such as terahertz (THz) and visible light communication (VLC), with the existing 5G millimeter-wave and sub-6 GHz base stations. This integration facilitates the creation of a network environment characterized by high transmission rates and extensive coverage. Our primary aim is to ensure the uninterrupted playback of real-time video applications for vehicle users. These video applications encompass video conferencing, live video, and on-demand video services. The outcomes of our simulations convincingly indicate that the proposed strategy adeptly addresses the challenge of bandwidth competition among vehicle users. Moreover, it notably boosts the efficient utilization of bandwidth from less crowded base stations, optimizes the fulfillment of bandwidth prerequisites for various video applications, and elevates the overall video quality experienced by users. Consequently, our findings serve as a successful validation of the practicality and effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

Simple Summary: Prostate cancer (PCa) is the most common cancer in men worldwide. Screening and diagnosis are based on prostate-specific antigen (PSA) blood testing and digital rectal examination. Nevertheless, these methods are not specific and have a high risk of mistaken results. This has led to overtreatment and unnecessary radical therapy; thus, better prognostic tools are urgently needed. In this view, microRNAs (miRs) appear as potential non-invasive biomarkers for PCa diagnosis, prognosis, and therapy. As the scientific literature available in this field is huge and very often controversial, we identified and discussed three topics that characterize the investigated research area by combining the big data from the literature together with a novel machine learning approach. By analyzing the papers clustered into these topics we have offered a deeper understanding of the current research, which helps to contribute to the advancement of this research field. Background: Prostate cancer (PCa) is the second leading cause of cancer-related deaths in men. Although the prostate-specific antigen (PSA) test is used in clinical practice for screening and/or early detection of PCa, it is not specific, thus resulting in high false-positive rates. MicroRNAs (miRs) provide an opportunity as biomarkers for diagnosis, prognosis, and recurrence of PCa. Because the size of the literature on it is increasing and often controversial, this study aims to consolidate the state-of-art of relevant published research. Methods: A Systematic Literature Review (SLR) approach was applied to analyze a set of 213 scientific publications through a text mining method that makes use of the Latent Dirichlet Allocation (LDA) algorithm. Results and Conclusions: The result of this activity, performed through the MySLR digital platform, allowed us to identify a set of three relevant topics characterizing the investigated research area. We analyzed and discussed all the papers clustered into them. We highlighted that several miRs are associated with PCa progression, and that their detection in patients' urine seems to be the more reliable and promising non-invasive tool for PCa diagnosis. Finally, we proposed some future research directions to help future scientists advance the field further. [ABSTRACT FROM AUTHOR]

• A feature selection method based on binary dandelion algorithm is proposed. • The algorithm applies a sine-cosine operator and a restart strategy. • Mutual information and quick bit mutation improve the performance of the algorithm. • The algorithm performs well on datasets with larger dimensions. Feature selection (FS) is an important data preprocessing technology for machine learning and data mining. Metaheuristic algorithm (MH) has been widely used in feature selection because of its powerful search function. This paper presents an improved Binary Dandelion Algorithm using Sine Cosine operator and Restart strategy (SCRBDA) for feature selection. First, the sine cosine operator is used in the radius formula of the core dandelions (CD), which significantly enhances the ability of algorithm development and exploration. Secondly, the algorithm uses a restart strategy to increase its ability to get rid of local optimum. Thirdly, mutual information is used to guide the generation of some dandelions, which pays more attention to the correlation between the selected features and categories. Finally, quick bit mutation is used as the mutation strategy to improve the diversity of the population. The SCRBDA proposed in this paper was tested on 18 datasets of different sizes from UCI machine learning database. The SCRBDA was compared with 8 other classical feature selection algorithms, and the performance of the proposed algorithm was evaluated through feature subset size, classification accuracy, fitness value, and F1-score. The experimental results show that SCRBDA achieves the best performance, which has stronger feature reduction ability and achieves better overall performance on most datasets. Especially on large-scale datasets, SCRBDA can obtain extremely smaller feature subsets while maintaining much higher classification accuracy, and satisfactory F1-score. [ABSTRACT FROM AUTHOR]

Data mining has become a relatively modern platform for information retrieval. The efficient data mining techniques can increase the reliability and accuracy of internal auditing for the various community even while lowering audit risk. Existing audit data mining approaches lack significant identification of hidden connections and interactions in bid data platforms. Hence, this study extends the literature survey on the signification of audit data mining in multiple applications. This survey identifies the scope of improved association algorithms in audit data mining, a rule-based machine learning approach to determine the exciting relationship among variables in large audit datasets. Therefore, a Conceptual Framework of Improved Association Algorithm (CFiAA) and its application in audit data mining is proposed. This study examines the strengths and weaknesses of the proposed CFiAA in audit mining. The proposed model has been trained using an audit data set and validates with various audit datasets. Finally, this paper presents the comparative analysis of the proposal to show its highest performance related to existing models. Thus, CFiAA scores the performance ratio of 94.5%, accuracy ratio of 92.4%, an efficiency ratio of 92.5%, F1 measure of 91.8%, error rate 32.5%, prediction ratio of 93.7%, and the precision ratio of 92.5% compared to existing models. [ABSTRACT FROM AUTHOR]

There are various applications of clustering in the fields of machine learning, data mining, data compression along with pattern recognition. The existent techniques like the Llyods algorithm (sometimes called k-means) were affected by the issue of the algorithm which converges to a local optimum along with no approximation guarantee. For overcoming these shortcomings, an efficient k-means clustering approach is offered by this paper for stream data mining. Coreset is a popular and fundamental concept for k-means clustering in stream data. In each step, reduction determines a coreset of inputs, and represents the error, where P represents number of input points according to nested property of coreset. Hence, a bit reduction in error of final coreset gets n times more accurate. Therefore, this motivated the author to propose a new coreset-reduction algorithm. The proposed algorithm executed on the Covertype dataset, Spambase dataset, Census 1990 dataset, Bigcross dataset, and Tower dataset. Our algorithm outperforms with competitive algorithms like Streamkm++, BICO (BIRCH meets Coresets for k-means clustering), and BIRCH (Balance Iterative Reducing and Clustering using Hierarchies. [ABSTRACT FROM AUTHOR]

Data clustering is a complex data mining problem that clusters a massive amount of data objects into a predefined number of clusters; in other words, it finds symmetric and asymmetric objects. Various optimization methods have been used to solve different machine learning problems. They usually suffer from local optimal problems and unbalance between the search mechanisms. This paper proposes a novel hybrid optimization method for solving various optimization problems. The proposed method is called HRSA, which combines the original Reptile Search Algorithm (RSA) and Remora Optimization Algorithm (ROA) and handles these mechanisms' search processes by a novel transition method. The proposed HRSA method aims to avoid the main weaknesses raised by the original methods and find better solutions. The proposed HRSA is tested on solving various complicated optimization problems—twenty-three benchmark test functions and eight data clustering problems. The obtained results illustrate that the proposed HRSA method performs significantly better than the original and comparative state-of-the-art methods. The proposed method overwhelmed all the comparative methods according to the mathematical problems. It obtained promising results in solving the clustering problems. Thus, HRSA has a remarkable efficacy when employed for various clustering problems. [ABSTRACT FROM AUTHOR]

In this paper, we develop a healthcare biclustering model in the field of healthcare to reduce the inconveniences linked to the data clustering on gene expression. The present study uses two separate healthcare biclustering approaches to identify specific gene activity in certain environments and remove the duplication of broad gene information components. Moreover, because of its adequacy in the problem where populations of potential solutions allow exploration of a greater portion of the research area, machine learning or heuristic algorithm has become extensively used for healthcare biclustering in the field of healthcare. The study is evaluated in terms of average match score for nonoverlapping modules, overlapping modules through the influence of noise for constant bicluster and additive bicluster, and the run time. The results show that proposed FCM blustering method has higher average match score, and reduced run time proposed FCM than the existing PSO-SA and fuzzy logic healthcare biclustering methods. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet and the rapid development of big data analysis technology, data mining has played a positive role in promoting industry and academia. Classification is an important problem in data mining. This paper explores the background and theory of support vector machines (SVM) in data mining classification algorithms and analyzes and summarizes the research status of various improved methods of SVM. According to the scale and characteristics of the data, different solution spaces are selected, and the solution of the dual problem is transformed into the classification surface of the original space to improve the algorithm speed. Research Process. Incorporating fuzzy membership into multicore learning, it is found that the time complexity of the original problem is determined by the dimension, and the time complexity of the dual problem is determined by the quantity, and the dimension and quantity constitute the scale of the data, so it can be based on the scale of the data Features Choose different solution spaces. The algorithm speed can be improved by transforming the solution of the dual problem into the classification surface of the original space. Conclusion. By improving the calculation rate of traditional machine learning algorithms, it is concluded that the accuracy of the fitting prediction between the predicted data and the actual value is as high as 98%, which can make the traditional machine learning algorithm meet the requirements of the big data era. It can be widely used in the context of big data. [ABSTRACT FROM AUTHOR]

Many real‐world data mining applications involve obtaining predictive models using imbalanced datasets. Frequently, the least common target variables present within datasets are associated with events that are highly relevant for end users. When these variables are nominal, we have a class‐imbalance problem which has been thoroughly studied within machine learning. As for regression tasks where target variables are continuous, few predictive models and evaluation techniques exist. This paper proposes a solution to these challenges. First, we introduce a cost‐sensitive learning algorithm based on a neural network trained on the minimization of a biased loss function. Results show a higher or comparable performance and convergence speed to existent techniques. Second, we develop new approaches for performance assessment of regression tasks within imbalanced domains by proposing new scalar measures, namely Geometric Mean Error (GME) and Class‐Weighted Error (CWE), as well as new graphical‐based measures, namely RECTPR, RECTNR, RECG − Mean and RECCWA curves. Unlike standard measures, our evaluation strategies are shown to be more robust to data imbalance as they reflect the performance of both rare and frequent events. [ABSTRACT FROM AUTHOR]

The early diagnosis of the diabetes condition is crucial for cure process, because an early diagnosis provides the ease of treatment for the patient and the physician. At this point, statistical methods and data mining algorithms can provide important opportunities for early diagnosis of diabetes mellitus. In the literature, many studies have been published for solution of this problem. In this study, firstly, these studies are analyzed in detail and classified according to their methodologies and solution approaches. The main aim of this paper is to provide the comprehensive and detailed review of the diagnosis of diabetes by statistical methods and machine learning algorithms. Also, this paper presents a literature review on the diagnosis diabetes up to the end of 2017. It's identified over 425 papers, highly cited 100 ones are presented in detailed. This paper provides to guide future research and knowledge accumulation and creation of classification and prediction techniques in diagnosis of diabetes. This study shows it is clear that the combination of different machine learning algorithms and optimization models can lead to more meaningful and powerful results. [ABSTRACT FROM AUTHOR]

This paper, based on differential privacy protecting K-means clustering algorithm, realizes privacy protection by adding data-disturbing Laplace noise to cluster center point. In order to solve the problem of Laplace noise randomness which causes the center point to deviate, especially when poor availability of clustering results appears because of small privacy budget parameters, an improved differential privacy protecting K-means clustering algorithm was raised in this paper. The improved algorithm uses the contour coefficients to quantitatively evaluate the clustering effect of each iteration and add different noise to different clusters. In order to be adapted to the huge number of data, this paper provides an algorithm design in MapReduce Framework. Experimental finding shows that the new algorithm improves the availability of the algorithm clustering results under the condition of ensuring individual privacy without significantly increasing its operating time. [ABSTRACT FROM AUTHOR]

Current suicide risk assessments for predicting suicide attempts are time consuming, of low predictive value and have inadequate reliability. This paper aims to develop a predictive model for suicide attempts among patients with depression using machine learning algorithms as well as presents a comparative study on single predictive models with ensemble predictive models for differentiating depressed patients with suicide attempts from non-suicide attempters. We applied and trained eight different machine learning algorithms using a dataset that consists of 75 patients diagnosed with a depressive disorder. A recursive feature elimination was used to reduce the features via three-fold cross validation. An ensemble predictive models outperformed the single predictive models. Voting and bagging revealed the highest accuracy of 92% compared to other machine learning algorithms. Our findings indicate that history of suicide attempt, religion, race, suicide ideation and severity of clinical depression are useful factors for prediction of suicide attempts. [ABSTRACT FROM AUTHOR]

Analysis of Titanic shipwreck is essential in order to understand the historical data. The correlation between the independent and dependent features was observed in order to determine features that may have impact on passenger survival. In this paper, we explored the Titanic data and four machine learning algorithms namely XGBoost, CatBoost, Decision trees, Random forests were implemented to predict survival rate of passengers. Some factors such as "Age", "Gender", "Children" played a key role towards the survival of Passengers. A comparative analysis between these algorithms was conducted and the models were evaluated by some metrics. Based on the analysis and metrics evaluation, XGBoost outperformed other implemented algorithms in this work. [ABSTRACT FROM AUTHOR]

With the convergence of various broadcasting systems, the amount of content available in mobile terminals including IPTV has significantly increased. In this paper, we propose a system that enables users to schedule programs considering both section video clips and full video clips based on the user detection method with similar preference. And, since the system constituting the contents can be classified according to the program, the proposed method can store a program desired by the user, and thus create and schedule a kind of individual channel. Experimental results show that the proposed method has a higher prediction accuracy; this is accomplished by comparing existing channel recommendation methods with the program recommendation methods proposed in this paper. [ABSTRACT FROM AUTHOR]

This paper addresses patient-reported outcomes (PROs) and telemonitoring in congestive heart failure (CHF), both increasingly important topics. The interest in CHF trials is shifting from hard end-points such as hospitalization and mortality, to softer end-points such health-related quality of life. However, the relation of these softer end-points to objective parameters is not well studied. Telemonitoring is suitable for collecting both patient-reported outcomes and objective parameters. Most telemonitoring studies, however, do not take full advantage of the available sensor technology and intelligent data analysis. The Chiron clinical observational study was performed among 24 CHF patients (17 men and 7 women, age 62.9 ± 9.4 years, 15 NYHA class II and 9 class III, 10 of ishaemic, aetiology, 6 dilated, 2 valvular, and 6 of multiple aetiologies or cardiomyopathy) in Italy and UK. A large number of physiological and ambient parameters were collected by wearable and other devices, together with PROs describing how well the patients felt, over 1,086 days of observation. The resulting data were mined for relations between the objective parameters and the PROs. The objective parameters (humidity, ambient temperature, blood pressure, SpO2, and sweeting intensity) could predict the PROs with accuracies up to 86% and AUC up to 0.83, making this the first report providing evidence for ambient and physiological parameters to be objectively related to PROs in CHF patients. We also analyzed the relations in the predictive models, gaining some insights into what affects the feeling of health, which was also generally not attempted in previous investigations. The paper strongly points to the possibility of using PROs as primary end-points in future trials. [ABSTRACT FROM AUTHOR]

This paper deals with strip packing metaheuristic algorithm selection using data mining techniques. Given a set of solved strip packing problem instances, the relationship between the instance characteristics and algorithm performance is learned and is used to predict the best algorithms to solve a new set of unseen problem instances. A framework capable of modelling this relationship for an automated packing algorithm selection is proposed. The effectiveness of the proposed framework is evaluated in the context of a large set of strip packing problem instances and the state-of-the-art strip packing algorithms. The results suggest a 91% accuracy in correctly identifying the best algorithm for a given instance. [ABSTRACT FROM AUTHOR]

Ex-ante online risk assessment for building emergency evacuation is essential to protect human life and property. Current risk assessment methods are limited by the tradeoff between accuracy and efficiency. In this paper, we propose an online method that overcomes this tradeoff based on multimedia data (e.g. videos data from surveillance cameras) and deep learning. The method consists of two parts. The first estimates the evacuee position as input for training the assessment model to then perform risk assessment in real scenarios. The second considers a social force model based on the evacuation simulation for the output of training model. We verify the proposed method in simulation and real scenarios. Model sensitivity analyses and large-scale tests demonstrate the usability and superiority of the proposed method. By the method, the computation time of risk assessment could be decreased from 10 minutes (by traditional simulation method) to 2.18 s. [ABSTRACT FROM AUTHOR]

Background: Manual extraction of information from electronic pathology (epath) reports to populate the Surveillance, Epidemiology, and End Result (SEER) database is labor intensive. Systematizing the data extraction automatically using machine-learning (ML) and natural language processing (NLP) is desirable to reduce the human labor required to populate the SEER database and to improve the timeliness of the data. This enables scaling up registry efficiency and collection of new data elements. To ensure the integrity, quality, and continuity of the SEER data, the misclassification error of ML and NPL algorithms needs to be negligible. Current algorithms fail to achieve the precision of human experts who can bring additional information in their assessments. Differences in registry format and the desire to develop a common information extraction platform further complicate the ML/NLP tasks. The purpose of our study is to develop triage rules to partially automate registry workflow to improve the precision of the auto-extracted information. Results: This paper presents a mathematical framework to improve the precision of a classifier beyond that of the Bayes classifier by selectively classifying item that are most likely to be correct. This results in a triage rule that only classifies a subset of the item. We characterize the optimal triage rule and demonstrate its usefulness in the problem of classifying cancer site from electronic pathology reports to achieve a desired precision. Conclusions: From the mathematical formalism, we propose a heuristic estimate for triage rule based on post-processing the soft-max output from standard machine learning algorithms. We show, in test cases, that the triage rule significantly improve the classification accuracy. [ABSTRACT FROM AUTHOR]

Big data classification is very slow when using traditional machine learning classifiers, particularly when using a lazy and slow-by-nature classifier such as the k-nearest neighbors algorithm (KNN). This paper proposes a new approach which is based on sorting the feature vectors of training data in a binary search tree to accelerate big data classification using the KNN approach. This is done using two methods, both of which utilize two local points to sort the examples based on their similarity to these local points. The first method chooses the local points based on their similarity to the global extreme points, while the second method chooses the local points randomly. The results of various experiments conducted on different big datasets show reasonable accuracy rates compared to state-of-the-art methods and the KNN classifier itself. More importantly, they show the high classification speed of both methods. This strong trait can be used to further improve the accuracy of the proposed methods. [ABSTRACT FROM AUTHOR]

The great attention given by the scientific community to multi-label learning in recent years has led to the development of a large number of methods, many of them based on ensembles. A comparison of the state-of-the-art in ensembles of multi-label classifiers over a wide set of 20 datasets have been carried out in this paper, evaluating their performance based on the characteristics of the datasets such as imbalance, dependence among labels and dimensionality. In each case, suggestions are given to choose the algorithm that fits best. Further, given the absence of taxonomies of ensembles of multi-label classifiers, a novel taxonomy for these methods is proposed. [ABSTRACT FROM AUTHOR]

Frequently, port scans are early indicators of more serious attacks. Unfortunately, the detection of slow port scans in company networks is challenging due to the massive amount of network data. This paper proposes an innovative approach for preprocessing flow-based data which is specifically tailored to the detection of slow port scans. The preprocessing chain generates new objects based on flow-based data aggregated over time windows while taking domain knowledge as well as additional knowledge about the network structure into account. The computed objects are used as input for the further analysis. Based on these objects, we propose two different approaches for detection of slow port scans. One approach is unsupervised and uses sequential hypothesis testing whereas the other approach is supervised and uses classification algorithms. We compare both approaches with existing port scan detection algorithms on the flow-based CIDDS-001 data set. Experiments indicate that the proposed approaches achieve better detection rates and exhibit less false alarms than similar algorithms. [ABSTRACT FROM AUTHOR]

Data mining and search-based algorithms have been applied to various problems due to their power and performance. There have been several studies on the use of these algorithms for refactoring. In this paper, we show how search based algorithms can be used for sequence diagram refactoring. We also show how a hybridized algorithm of Kmeans and Simulated Annealing (SA) algorithms can aid each other in solving sequence diagram refactoring. Results show that search based algorithms can be used successfully in refactoring sequence diagram on small and large case studies. In addition, the hybridized algorithm obtains good results using selected quality metrics. Detailed insights on the experiments on sequence diagram refactoring reveal that the limitations of SA can be addressed by hybridizing the Kmeans algorithm to the SA algorithm. [ABSTRACT FROM AUTHOR]

Traditional methods for monitoring influenza are haphazard and lack fine-grained details regarding the spatial and temporal dynamics of outbreaks. Twitter gives researchers and public health officials an opportunity to examine the spread of influenza in real-time and at multiple geographical scales. In this paper, we introduce an improved framework for monitoring influenza outbreaks using the social media platform Twitter. Relying upon techniques from geographic information science (GIS) and data mining, Twitter messages were collected, filtered, and analyzed for the thirty most populated cities in the United States during the 2013–2014 flu season. The results of this procedure are compared with national, regional, and local flu outbreak reports, revealing a statistically significant correlation between the two data sources. The main contribution of this paper is to introduce a comprehensive data mining process that enhances previous attempts to accurately identify tweets related to influenza. Additionally, geographical information systems allow us to target, filter, and normalize Twitter messages. [ABSTRACT FROM AUTHOR]

For high-dimensional and massive data sets, traditional centralized gene expression programming (GEP) or improved algorithms lead to increased run-time and decreased prediction accuracy. To solve this problem, this paper proposes a new improved algorithm called distributed function mining for gene expression programming based on fast reduction (DFMGEP-FR). In DFMGEP-FR, fast attribution reduction in binary search algorithms (FAR-BSA) is proposed to quickly find the optimal attribution set, and the function consistency replacement algorithm is given to solve integration of the local function model. Thorough comparative experiments for DFMGEP-FR, centralized GEP and the parallel gene expression programming algorithm based on simulated annealing (parallel GEPSA) are included in this paper. For the waveform, mushroom, connect-4 and musk datasets, the comparative results show that the average time-consumption of DFMGEP-FR drops by 89.09%%, 88.85%, 85.79% and 93.06%, respectively, in contrast to centralized GEP and by 12.5%, 8.42%, 9.62% and 13.75%, respectively, compared with parallel GEPSA. Six well-studied UCI test data sets demonstrate the efficiency and capability of our proposed DFMGEP-FR algorithm for distributed function mining. [ABSTRACT FROM AUTHOR]

Bayesian network classifiers (BNCs) have demonstrated competitive classification performance in a variety of real-world applications. A highly scalable BNC with high expressivity is extremely desirable. This paper proposes Redundant Dependence Elimination (RDE) for improving the classification performance and expressivity of k-dependence Bayesian classifier (KDB). To demonstrate the unique characteristics of each case, RDE identifies redundant conditional dependencies and then substitute/remove them. The learned personalized k-dependence Bayesian Classifier (PKDB) can achieve high-confidence conditional probabilities, and graphically interpret the dependency relationships between attributes. Two thyroid cancer datasets and four other cancer datasets from the UCI machine learning repository are selected for our experimental study. The experimental results prove the effectiveness of the proposed algorithm in terms of zero-one loss, bias, variance and AUC. [ABSTRACT FROM AUTHOR]

Cost-sensitive feature selection learning is an important preprocessing step in machine learning and data mining. Recently, most existing cost-sensitive feature selection algorithms are heuristic algorithms, which evaluate the importance of each feature individually and select features one by one. Obviously, these algorithms do not consider the relationship among features. In this paper, we propose a new algorithm for minimal cost feature selection called the rough sets and Laplacian score based cost-sensitive feature selection. The importance of each feature is evaluated by both rough sets and Laplacian score. Compared with heuristic algorithms, the proposed algorithm takes into consideration the relationship among features with locality preservation of Laplacian score. We select a feature subset with maximal feature importance and minimal cost when cost is undertaken in parallel, where the cost is given by three different distributions to simulate different applications. Different from existing cost-sensitive feature selection algorithms, our algorithm simultaneously selects out a predetermined number of “good” features. Extensive experimental results show that the approach is efficient and able to effectively obtain the minimum cost subset. In addition, the results of our method are more promising than the results of other cost-sensitive feature selection algorithms. [ABSTRACT FROM AUTHOR]

Clustering algorithms (i.e., Gaussian mixture models, k-means) tackle the problem of grouping a set of elements in such a way that elements from the same group (or cluster) have more similar properties to each other than to those elements in other clusters. This simple concept turns out to be the basis in complex algorithms from many application areas, including sequence analysis and genotyping in bioinformatics, medical imaging, antimicrobial activity, market research, social networking, etc. However, as the data volume continues to increase, the performance of clustering algorithms is heavily influenced by the memory subsystem. In this paper, we propose a novel and efficient implementation of Lloyd’s k-means clustering algorithm to substantially reduce data movement along the memory hierarchy. Our contributions are based on the fact that the vast majority of processors are equipped with powerful Single Instruction Multiple Data (SIMD) instructions that are, in most cases, underused. SIMD improves the CPU computational power and, if used wisely, can be seen as an opportunity to improve on the application data transfers by compressing/decompressing the data, specially for memory-bound applications. Our contributions include a SIMD-friendly data layout organization, in-register implementation of key functions and SIMD-based compression. We demonstrate that using our optimized SIMD-based compression method, it is possible to improve the performance and energy of k-means by a factor of 4.5x and 8.7x, respectively, for a i7 Haswell machine, and 22x and 22.2x for Xeon Phi: KNL, running a single thread. [ABSTRACT FROM AUTHOR]

Sentiment analysis techniques are increasingly exploited to categorize the opinion text to one or more predefined sentiment classes for the creation and automated maintenance of review-aggregation websites. In this paper, a Malay sentiment analysis classification model is proposed to improve classification performances based on the semantic orientation and machine learning approaches. First, a total of 2,478 Malay sentiment-lexicon phrases and words are assigned with a synonym and stored with the help of more than one Malay native speaker, and the polarity is manually allotted with a score. In addition, the supervised machine learning approaches and lexicon knowledge method are combined for Malay sentiment classification with evaluating thirteen features. Finally, three individual classifiers and a combined classifier are used to evaluate the classification accuracy. In experimental results, a wide-range of comparative experiments is conducted on a Malay Reviews Corpus (MRC), and it demonstrates that the feature extraction improves the performance of Malay sentiment analysis based on the combined classification. However, the results depend on three factors, the features, the number of features and the classification approach. [ABSTRACT FROM AUTHOR]

Social media are becoming an increasingly important source of information about the public mood regarding issues such as elections, Brexit, stock market, etc. In this paper we focus on sentiment classification of Twitter data. Construction of sentiment classifiers is a standard text mining task, but here we address the question of how to properly evaluate them as there is no settled way to do so. Sentiment classes are ordered and unbalanced, and Twitter produces a stream of time-ordered data. The problem we address concerns the procedures used to obtain reliable estimates of performance measures, and whether the temporal ordering of the training and test data matters. We collected a large set of 1.5 million tweets in 13 European languages. We created 138 sentiment models and out-of-sample datasets, which are used as a gold standard for evaluations. The corresponding 138 in-sample datasets are used to empirically compare six different estimation procedures: three variants of cross-validation, and three variants of sequential validation (where test set always follows the training set). We find no significant difference between the best cross-validation and sequential validation. However, we observe that all cross-validation variants tend to overestimate the performance, while the sequential methods tend to underestimate it. Standard cross-validation with random selection of examples is significantly worse than the blocked cross-validation, and should not be used to evaluate classifiers in time-ordered data scenarios. [ABSTRACT FROM AUTHOR]

Advanced Metering Infrastructure (AMI) realizes a two-way communication of electricity data through by interconnecting with a computer network as the core component of the smart grid. Meanwhile, it brings many new security threats and the traditional intrusion detection method can’t satisfy the security requirements of AMI. In this paper, an intrusion detection system based on Online Sequence Extreme Learning Machine (OS-ELM) is established, which is used to detecting the attack in AMI and carrying out the comparative analysis with other algorithms. Simulation results show that, compared with other intrusion detection methods, intrusion detection method based on OS-ELM is more superior in detection speed and accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we propose SUbsequence Matching framework with cell MERgence (SUMMER) for online subsequence matching between histogram-based stream synopsis structures under the dynamic time warping distance. Given a query synopsis pattern, SUMMER continuously identifies all the matching subsequences for a stream as the bins are generated. To effectively reduce the computation time, we design a Weighted Dynamic Time Warping (WDTW) algorithm, which computes the warping distance directly between two histogram-based synopses. Furthermore, a Stack-based Overlapping Filter Algorithm (SOFA) is provided to remove the overlapping subsequences to avoid the redundant information. Finally, we design an optional refinement module to relax the subsequence range limit and improve the matching accuracy. Our experiments on real datasets show that the proposed method significantly speeds up the pattern matching without compromising the accuracy required when compared with other approaches. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new automatic hyperparameter selection approach for determining the optimal network configuration (network structure and hyperparameters) for deep neural networks using particle swarm optimization (PSO) in combination with a steepest gradient descent algorithm. In the proposed approach, network configurations were coded as a set of real-number m-dimensional vectors as the individuals of the PSO algorithm in the search procedure. During the search procedure, the PSO algorithm is employed to search for optimal network configurations via the particles moving in a finite search space, and the steepest gradient descent algorithm is used to train the DNN classifier with a few training epochs (to find a local optimal solution) during the population evaluation of PSO. After the optimization scheme, the steepest gradient descent algorithm is performed with more epochs and the final solutions (pbest and gbest) of the PSO algorithm to train a final ensemble model and individual DNN classifiers, respectively. The local search ability of the steepest gradient descent algorithm and the global search capabilities of the PSO algorithm are exploited to determine an optimal solution that is close to the global optimum. We constructed several experiments on hand-written characters and biological activity prediction datasets to show that the DNN classifiers trained by the network configurations expressed by the final solutions of the PSO algorithm, employed to construct an ensemble model and individual classifier, outperform the random approach in terms of the generalization performance. Therefore, the proposed approach can be regarded an alternative tool for automatic network structure and parameter selection for deep neural networks. [ABSTRACT FROM AUTHOR]

In this article, we propose a new data mining algorithm, by which one can both capture the non-linearity in data and also find the best subset model. To produce an enhanced subset of the original variables, a preferred selection method should have the potential of adding a supplementary level of regression analysis that would capture complex relationships in the data via mathematical transformation of the predictors and exploration of synergistic effects of combined variables. The method that we present here has the potential to produce an optimal subset of variables, rendering the overall process of model selection more efficient. This algorithm introduces interpretable parameters by transforming the original inputs and also a faithful fit to the data. The core objective of this paper is to introduce a new estimation technique for the classical least square regression framework. This new automatic variable transformation and model selection method could offer an optimal and stable model that minimizes the mean square error and variability, while combining all possible subset selection methodology with the inclusion variable transformations and interactions. Moreover, this method controls multicollinearity, leading to an optimal set of explanatory variables. [ABSTRACT FROM AUTHOR]

It is difficult for learning models to achieve high classification performances with imbalanced data sets, because with imbalanced data sets, when one of the classes is much larger than the others, most machine learning and data mining classifiers are overly influenced by the larger classes and ignore the smaller ones. As a result, the classification algorithms often have poor learning performances due to slow convergence in the smaller classes. To balance such data sets, this paper presents a strategy that involves reducing the sizes of the majority data and generating synthetic samples for the minority data. In the reducing operation, we use the box-and-whisker plot approach to exclude outliers and the Mega-Trend-Diffusion method to find representative data from the majority data. To generate the synthetic samples, we propose a counterintuitive hypothesis to find the distributed shape of the minority data, and then produce samples according to this distribution. Four real datasets were used to examine the performance of the proposed approach. We used paired t-tests to compare the Accuracy, G-mean, and F-measure scores of the proposed data pre-processing (PPDP) method merging in the D3C method (PPDP+D3C) with those of the one-sided selection (OSS), the well-known SMOTEBoost (SB) study, and the normal distribution-based oversampling (NDO) approach, and the proposed data pre-processing (PPDP) method. The results indicate that the classification performance of the proposed approach is better than that of above-mentioned methods. [ABSTRACT FROM AUTHOR]

In Data Mining, during the preprocessing step, there is a considerable diversity of candidate algorithms to select important features, according to some criteria. This broad availability of algorithms that perform the Feature Selection task gives rise to the difficulty of choosing, a priori , between the algorithms at hand, the most promising one for a particular problem. In this paper, we present the proposal and evaluation of a new architecture for the recommendation of Feature Selection algorithms based on the use of Metalearning. Our framework is very flexible since the user can adapt it to its proper needs. This flexibility is one of the main advantages of our proposal over other approaches in the literature, which involve steps that cannot be adapted to the user’s local requirements. Furthermore, it combines several concepts of intelligent systems, including Machine Learning and Data Mining, with topics derived from expert systems, as user and data-driven knowledge, with meta-knowledge. This set of solutions coupled with leading-edge technologies allows our architecture to be integrated into any information system, which impact on the automation of services and in reducing human effort during the process. Regarding the Metalearning process, our framework considers several types of properties inherent to the data sets, as well as, Feature Selection algorithms based on many information, distance, dependence and consistency measures. The quality of the methods for Feature Selection was estimated according to a multicriteria performance measure, which guided the ranking process of these algorithms for the construction of data metabases. Proposed by the authors of this work, this multicriteria performance measure combines any three measurements on a single one, creating an interesting and powerful tool to evaluate not only FS algorithms but also to assess any context where it is necessary a combination to maximize a measure or minimize it. The recommendation models, represented by decision trees and induced from the training metabases, allowed us to see in what circumstances a Feature Selection algorithm outperforms the other and what aspects of the data present greater influence in determining the performance of these algorithms. Nevertheless, if the user wishes, any other learning algorithm may be used to induce the recommendation model. This versatility is another strong point of this proposal. Results show that with the characterization of data, through statistical, information and complexity measures, it is possible to reach an accuracy higher than 90%. Besides yielding recommendation models that are interpretable and robust to overfitting, the developed architecture is less computationally expensive than approaches recently proposed in the literature. [ABSTRACT FROM AUTHOR]

In many computer vision and machine learning applications, the data sets distribute on certain low-dimensional subspaces. Subspace clustering is a powerful technology to find the underlying subspaces and cluster data points correctly. However, traditional subspace clustering methods can only be applied on data from one source, and how to extend these methods and enable the extensions to combine information from various data sources has become a hot area of research. Previous multi-view subspace methods aim to learn multiple subspace representation matrices simultaneously and these learning task for different views are treated equally. After obtaining representation matrices, they stack up the learned representation matrices as the common underlying subspace structure. However, for many problems, the importance of sources and the importance of features in one source both can be varied, which makes the previous approaches ineffective. In this paper, we propose a novel method called Robust Auto-weighted Multi-view Subspace Clustering (RAMSC). In our method, the weight for both the sources and features can be learned automatically via utilizing a novel trick and introducing a sparse norm. More importantly, the objective of our method is a common representation matrix which directly reflects the common underlying subspace structure. A new efficient algorithm is derived to solve the formulated objective with rigorous theoretical proof on its convergency. Extensive experimental results on five benchmark multi-view datasets well demonstrate that the proposed method consistently outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The field of wireless sensor networks (WSNs), embedded systems with sensing and networking capability, has now matured after a decade-long research effort and technological advances in electronics and networked systems. An important remaining challenge now is to extract meaningful information from the ever-increasing amount of sensor data collected by WSNs. In particular, there is strong interest in algorithms capable of automatic detection of patterns, events or other out-of-the order, anomalous system behavior. Data anomalies may indicate states of the system that require further analysis or prompt actions. Traditionally, anomaly detection techniques are executed in a central processing facility, which requires the collection of all measurement data at a central location, an obvious limitation for WSNs due to the high data communication costs involved. In this paper we explore the extent by which one may depart from this classical centralized paradigm, looking at decentralized anomaly detection based on unsupervised machine learning. Our aim is to detect anomalies at the sensor nodes, as opposed to centrally, to reduce energy and spectrum consumption. We study the information gain coming from aggregate neighborhood data, in comparison to performing simple, in-node anomaly detection. We evaluate the effects of neighborhood size and spatio-temporal correlation on the performance of our new neighborhood-based approach using a range of real-world network deployments and datasets. We find the conditions that make neighborhood data fusion advantageous, identifying also the cases in which this approach does not lead to detectable improvements. Improvements are linked to the diffusive properties of data (spatio-temporal correlations) but also to the type of sensors, anomalies and network topological features. Overall, when a dataset stems from a similar mixture of diffusive processes precision tends to benefit, particularly in terms of recall. Our work paves the way towards understanding how distributed data fusion methods may help managing the complexity of wireless sensor networks, for instance in massive Internet of Things scenarios. [ABSTRACT FROM AUTHOR]

Point-of-Interest recommendation is an essential means to help people discover attractive locations, especially when people travel out of town or to unfamiliar regions. While a growing line of research has focused on modeling user geographical preferences for POI recommendation, they ignore the phenomenon of user interest drift across geographical regions, i.e., users tend to have different interests when they travel in different regions, which discounts the recommendation quality of existing methods, especially for out-of-town users. In this paper, we propose a latent class probabilistic generative model Spatial-Temporal LDA (ST-LDA) to learn region-dependent personal interests according to the contents of their checked-in POIs at each region. As the users’ check-in records left in the out-of-town regions are extremely sparse, ST-LDA incorporates the crowd’s preferences by considering the public’s visiting behaviors at the target region. To further alleviate the issue of data sparsity, a social-spatial collective inference framework is built on ST-LDA to enhance the inference of region-dependent personal interests by effectively exploiting the social and spatial correlation information. Besides, based on ST-LDA, we design an effective attribute pruning (AP) algorithm to overcome the curse of dimensionality and support fast online recommendation for large-scale POI data. Extensive experiments have been conducted to evaluate the performance of our ST-LDA model on two real-world and large-scale datasets. The experimental results demonstrate the superiority of ST-LDA and AP, compared with the state-of-the-art competing methods, by making more effective and efficient mobile recommendations. [ABSTRACT FROM PUBLISHER]

With the advent of Big Data, data is being collected at an unprecedented fast pace, and it needs to be processed in a short time. To deal with data streams that flow continuously, classical batch learning algorithms cannot be applied and it is necessary to employ online approaches. Online learning consists of continuously revising and refining a model by incorporating new data as they arrive, and it allows important problems such as concept drift or management of extremely high-dimensional datasets to be solved. In this paper, we present a unified pipeline for online learning which covers online discretization, feature selection and classification. Three classical methods—the k -means discretizer, the χ 2 filter and a one-layer artificial neural network—have been reimplemented to be able to tackle online data, showing promising results on both synthetic and real datasets. [ABSTRACT FROM AUTHOR]

Abstract: Quite often, in order to derive meaningful insights, accounting researchers have to analyze large bodies of text. Usually, this is done manually by several human coders, which makes the process time consuming, expensive, and often neither replicable nor accurate. In an attempt to mitigate these problems, we perform a feasibility study investigating the applicability of computer-aided content analysis techniques onto the domain of accounting research. defines an algorithm’s reliability as its stability, reproducibility and accuracy. Since in computer-aided text classification, which is inherently objective and repeatable, the first two requirements, stability and reproducibility, are not an issue, this paper focuses exclusively on the third requirement, the algorithm’s accuracy. It is important to note that, although inaccurate classification results are completely worthless, it is surprising to see how few research papers actually mention the accuracy of the used classification methodology. After a survey of the available techniques, we perform an in depth analysis of the most promising one, LPU (Learning from Positive and Unlabelled), which turns out to have an F-value and accuracy of about 90%, which means that, given a random text, it has a 90% probability of classifying it correctly. [Copyright &y& Elsevier]

This paper compares the performance of several boosting and bagging techniques in the context of learning from imbalanced and noisy binary-class data. Noise and class imbalance are two well-established data characteristics encountered in a wide range of data mining and machine learning initiatives. The learning algorithms studied in this paper, which include SMOTEBoost, RUSBoost, Exactly Balanced Bagging, and Roughly Balanced Bagging, combine boosting or bagging with data sampling to make them more effective when data are imbalanced. These techniques are evaluated in a comprehensive suite of experiments, for which nearly four million classification models were trained. All classifiers are assessed using seven different performance metrics, providing a complete perspective on the performance of these techniques, and results are tested for statistical significance via analysis-of-variance modeling. The experiments show that the bagging techniques generally outperform boosting, and hence in noisy data environments, bagging is the preferred method for handling class imbalance. [ABSTRACT FROM PUBLISHER]

Abstract: Because of the varying and dynamic characteristics of network traffic, such as fast transfer, huge volume, shot-lived, inestimable and infinite, it is a serious challenge for network administrators to monitor network traffic in real time and judge whether the whole network works well. Currently, most of the existing techniques in this area are based on signature training, learning or matching, which may be too complicated to satisfy timely requirements. Other statistical methods including sampling, hashing or counting are all approximate methods and compute an incomplete set of results. Since the main objective of network monitoring is to discover and understand the active events that happen frequently and may influence or even ruin the total network. So in the paper we aim to use the technique of frequent pattern mining to find out these events. We first design a sliding window model to make sure the mining result novel and integrated; then, under the consideration of the distribution and fluidity of network flows, we develop a powerful class of algorithms that contains vertical re-mining algorithm, multi-pattern re-mining algorithm, fast multi-pattern capturing algorithm and fast multi-pattern capturing supplement algorithm to deal with a series of problems when applying frequent pattern mining algorithm in network traffic analysis. Finally, we develop a monitoring system to evaluate our algorithms on real traces collected from the campus network of Peking University. The results show that some given algorithms are effective enough and our system can definitely identify a lot of potentially very valuable information in time which greatly help network administrators to understand regular applications and detect network anomalies. So the research in this paper not only provides a new application area for frequent pattern mining, but also provides a new technique for network monitoring. [ABSTRACT FROM AUTHOR]