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In practical data mining, a wide range of classification algorithms is employed for prediction tasks. However, selecting the best algorithm poses a challenging task for machine learning practitioners and experts, primarily due to the inherent variability in the characteristics of classification problems, referred to as datasets, and the unpredictable performance of these algorithms. Dataset characteristics are quantified in terms of meta-features, while classifier performance is evaluated using various performance metrics. The assessment of classifiers through empirical methods across multiple classification datasets, while considering multiple performance metrics, presents a computationally expensive and time-consuming obstacle in the pursuit of selecting the optimal algorithm. Furthermore, the scarcity of sufficient training data, denoted by dimensions representing the number of datasets and the feature space described by meta-feature perspectives, adds further complexity to the process of algorithm selection using classical machine learning methods. This research paper presents an integrated framework called eML-CBR that combines edge edge-ML and case-based reasoning methodologies to accurately address the algorithm selection problem. It adapts a multi-level, multi-view case-based reasoning methodology, considering data from diverse feature dimensions and the algorithms from multiple performance aspects, that distributes computations to both cloud edges and centralized nodes. On the edge, the first-level reasoning employs machine learning methods to recommend a family of classification algorithms, while at the second level, it recommends a list of the top-k algorithms within that family. This list is further refined by an algorithm conflict resolver module. The eML-CBR framework offers a suite of contributions, including integrated algorithm selection, multi-view meta-feature extraction, innovative performance criteria, improved algorithm recommendation, data scarcity mitigation through incremental learning, and an open-source CBR module, reshaping research paradigms. The CBR module, trained on 100 datasets and tested with 52 datasets using 9 decision tree algorithms, achieved an accuracy of 94% for correct classifier recommendations within the top k=3 algorithms, making it highly suitable for practical classification applications. [ABSTRACT FROM AUTHOR]

Multi-label learning has widely applied in machine learning and data mining. The purpose of feature selection is to select an approximately optimal feature subset to characterize the original feature space. Similar to single-label data, feature selection is an import preprocessing step to enhance the performance of multi-label classification model. In this paper, we propose a multi-label feature selection approach with Pareto optimality for continuous data, called MLFSPO. It maps multi-label features to high-dimensional space to evaluate the correlation between features and labels by utilizing the Hilbert-Schmidt Independence Criterion (HSIC). Then, the feature subset obtains by combining the Pareto optimization with feature ordering criteria and label weighting. Eventually, extensive experimental results on publicly available data sets show the effectiveness of the proposed algorithm in multi-label tasks. [ABSTRACT FROM AUTHOR]

Density-based clustering has received increasing attention for its ability to handle clusters of arbitrary shapes. However, it still has difficulties in mining clusters of arbitrary densities, especially the clusters of sparse regions in the presence of dense regions. To address this problem, this paper presents a new concept called density decreased chain on the mutual k-NN graph. It starts with the local density center whose density is the highest in the data points connected to this center. Based on the density decreased chain, the concept of the core point is redefined. The density of the core point is close to that of the local density center on the same density decreased chain as the core point. According to its definition, the core point in the data with arbitrary densities can be well identified because the local density centers exist in both sparse and dense regions. Further, intra-cluster density decreased chain is defined to mine subclusters in the core points. After forming the subclusters, the remaining data point is hierarchically assigned to one of these subclusters by the density decreased chains containing this remaining data point. The experiments illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper provides a unified description of Widening, a framework for the use of parallel (or otherwise abundant) computational resources to improve model quality. We discuss different theoretical approaches to Widening with and without consideration of diversity. We then soften some of the underlying constraints so that Widening can be implemented in real world algorithms. We summarize earlier experimental results demonstrating the potential impact as well as promising implementation strategies before concluding with a survey of related work. [ABSTRACT FROM AUTHOR]

Reports are the standard way of communication between the radiologist and the referring clinician. Efforts are made to improve this communication by, for instance, introducing standardization and structured reporting. Natural Language Processing (NLP) is another promising tool which can improve and enhance the radiological report by processing free text. NLP as such adds structure to the report and exposes the information, which in turn can be used for further analysis. This paper describes pre-processing and processing steps and highlights important challenges to overcome in order to successfully implement a free text mining algorithm using NLP tools and machine learning in a small language area, like Dutch. A rule-based algorithm was constructed to classify T-stage of pulmonary oncology from the original free text radiological report, based on the items tumor size, presence and involvement according to the 8th TNM classification system. PyContextNLP, spaCy and regular expressions were used as tools to extract the correct information and process the free text. Overall accuracy of the algorithm for evaluating T-stage was 0,83 in the training set and 0,87 in the validation set, which shows that the approach in this pilot study is promising. Future research with larger datasets and external validation is needed to be able to introduce more machine learning approaches and perhaps to reduce required input efforts of domain-specific knowledge. However, a hybrid NLP approach will probably achieve the best results. [ABSTRACT FROM AUTHOR]

Multi-label sorting learning has been successful in many fields. It can not only express the complex semantic information of learning objects, but also present good generalization ability in dealing with complex things. This paper proposes age estimation algorithm of facial images based on multi-label sorting. This estimation algorithm is for the lack of facial age dataset, and it changes the traditional multi-valued classification method, simplified the problem of tedious steps to estimate age and shortened the time for model training. A series of experiments on two age datasets shows that the algorithm has achieved very good results in evaluating indicators, and these indicators include MAE (mean absolute error), CS (cumulative score), and convergence rate. When compared with some classic algorithms of age estimation, the efficiency and accuracy of the algorithm are verified. [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]

Association rule mining is an important topic in data mining. The problem is to discover all (or almost all) associations among items in the transaction database that satisfy some user-specified constraints. Usually, the constraints are related to minimal support and minimal confidence. Class association rules (CARs) are a special type of association rules that can be applied for classification problem. Previous research showed that classification based on association rules has higher accuracy than can be achieved with an inductive learning algorithm or C4.5. As such, many methods have been proposed for mining CARs, although these use batch processing. However, datasets are often changed, with records added or/and deleted, and consequently updating CARs is a challenging problem. This paper proposes an efficient method for updating CARs when records are deleted. First, we use an MECR-tree to store nodes for the original dataset. The information in the nodes of this tree are updated based on the deleted records. Second, the concept of pre-large itemsets is used to avoid rescanning the original dataset. Finally, we propose an algorithm to efficiently update and generate CARs. We also analyze the time complexity to show the efficiency of our proposed algorithm. The experimental results show that the proposed method outperforms mining CARs from the dataset after record deletion. [ABSTRACT FROM AUTHOR]

Language models (LMs) such as BERT and GPT have revolutionized natural language processing (NLP). However, the medical field faces challenges in training LMs due to limited data access and privacy constraints imposed by regulations like the Health Insurance Portability and Accountability Act (HIPPA) and the General Data Protection Regulation (GDPR). Federated learning (FL) offers a decentralized solution that enables collaborative learning while ensuring data privacy. In this study, we evaluated FL on 2 biomedical NLP tasks encompassing 8 corpora using 6 LMs. Our results show that: (1) FL models consistently outperformed models trained on individual clients' data and sometimes performed comparably with models trained with polled data; (2) with the fixed number of total data, FL models training with more clients produced inferior performance but pre-trained transformer-based models exhibited great resilience. (3) FL models significantly outperformed pre-trained LLMs with few-shot prompting. [ABSTRACT FROM AUTHOR]

An ensemble in machine learning is defined as a set of models (such as classifiers or predictors) that are induced individually from data by using one or more machine learning algorithms for a given task and then work collectively in the hope of generating improved decisions. In this paper we investigate the factors that influence ensemble performance, which mainly include accuracy of individual classifiers, diversity between classifiers, the number of classifiers in an ensemble and the decision fusion strategy. Among them, diversity is believed to be a key factor but more complex and difficult to be measured quantitatively, and it was thus chosen as the focus of this study, together with the relationships between the other factors. A technique was devised to build ensembles with decision trees that are induced with randomly selected features. Three sets of experiments were performed using 12 benchmark datasets, and the results indicate that (i) a high level of diversity indeed makes an ensemble more accurate and robust compared with individual models; (ii) small ensembles can produce results as good as, or better than, large ensembles provided the appropriate (e.g. more diverse) models are selected for the inclusion. This has implications that for scaling up to larger databases the increased efficiency of smaller ensembles becomes more significant and beneficial. As a test case study, ensembles are built based on these findings for a real world application-osteoporosis classification, and found that, in each case of three datasets used, the ensembles out-performed individual decision trees consistently and reliably. [ABSTRACT FROM AUTHOR]

Incremental learning has been used extensively for data stream classification. Most attention on the data stream classification paid on non-evolutionary methods. In this paper, we introduce new incremental learning algorithms based on harmony search. We first propose a new classification algorithm for the classification of batch data called harmony-based classifier and then give its incremental version for classification of data streams called incremental harmony-based classifier. Finally, we improve it to reduce its computational overhead in absence of drifts and increase its robustness in presence of noise. This improved version is called improved incremental harmony-based classifier. The proposed methods are evaluated on some real world and synthetic data sets. Experimental results show that the proposed batch classifier outperforms some batch classifiers and also the proposed incremental methods can effectively address the issues usually encountered in the data stream environments. Improved incremental harmony-based classifier has significantly better speed and accuracy on capturing concept drifts than the non-incremental harmony based method and its accuracy is comparable to non-evolutionary algorithms. The experimental results also show the robustness of improved incremental harmony-based classifier. [ABSTRACT FROM AUTHOR]

Data preparation is an important step in mining incomplete data. To deal with this problem, this paper introduces a new imputation approach called SN (Shell Neighbors) imputation, or simply SNI. The SNI fills in an incomplete instance (with missing values) in a given dataset by only using its left and right nearest neighbors with respect to each factor (attribute), referred them to Shell Neighbors. The left and right nearest neighbors are selected from a set of nearest neighbors of the incomplete instance. The size of the sets of the nearest neighbors is determined with the cross-validation method. And then the SNI is generalized to deal with missing data in datasets with mixed attributes, for example, continuous and categorical attributes. Some experiments are conducted for evaluating the proposed approach, and demonstrate that the generalized SNI method outperforms the kNN imputation method at imputation accuracy and classification accuracy. [ABSTRACT FROM AUTHOR]

The objective of this paper is to present and discuss a link mining algorithm called CorpInterlock and its application to the financial domain. This algorithm selects the largest strongly connected component of a social network and ranks its vertices using several indicators of distance and centrality. These indicators are merged with other relevant indicators in order to forecast new variables using a boosting algorithm. We applied the algorithm CorpInterlock to integrate the metrics of an extended corporate interlock (social network of directors and financial analysts) with corporate fundamental variables and analysts’ predictions (consensus). CorpInterlock used these metrics to forecast the trend of the cumulative abnormal return and earnings surprise of S&P 500 companies. The rationality behind this approach is that the corporate interlock has a direct effect on future earnings and returns because these variables affect directors and managers’ compensation. The financial analysts engage in what the agency theory calls the “earnings game”: Managers want to meet the financial forecasts of the analysts and analysts want to increase their compensation or business of the company that they follow. Following the CorpInterlock algorithm, we calculated a group of well-known social network metrics and integrated with economic variables using Logitboost. We used the results of the CorpInterlock algorithm to evaluate several trading strategies. We observed an improvement of the Sharpe ratio (risk-adjustment return) when we used “long only” trading strategies with the extended corporate interlock instead of the basic corporate interlock before the regulation Fair Disclosure (FD) was adopted (1998–2001). There was no major difference among the trading strategies after 2001. Additionally, the CorpInterlock algorithm implemented with Logitboost showed a significantly lower test error than when the CorpInterlock algorithm was implemented with logistic regression. We conclude that the CorpInterlock algorithm showed to be an effective forecasting algorithm and supported profitable trading strategies. [ABSTRACT FROM AUTHOR]

In multi-instance learning, the training set comprises labeled bags that are composed of unlabeled instances, and the task is to predict the labels of unseen bags. In this paper, a web mining problem, i.e. web index recommendation, is investigated from a multi-instance view. In detail, each web index page is regarded as a bag, while each of its linked pages is regarded as an instance. A user favoring an index page means that he or she is interested in at least one page linked by the index. Based on the browsing history of the user, recommendation could be provided for unseen index pages. An algorithm named Fretcit-κNN, which employs the Minimal Hausdorff distance between frequent term sets and utilizes both the references and citers of an unseen bug in determining its label, is proposed to solve the problem. Experiments show that in average the recommendation accuracy of Fretcit-κNN is 81.0% with 71.7% recall and 70.9% precision, which is significantly better than the best algorithm that does not consider the specific characteristics of multi-instance learning, whose performance is 76.3% accuracy with 63.4% recall and 66.1% precision. [ABSTRACT FROM AUTHOR]

The fields of data science and data mining are enduring high-dimensionality issues because of a high volume of data. Conventional machine learning techniques give disgruntled responses to high-dimensional datasets. Feature selection is used to get the appropriate information from the dataset to reduce the dimensionality of the data. The recently proposed Salp Swarm Algorithm (SSA) is a population-based meta-heuristic optimization technique inspired by the Sea Salps Swarming technique. SSA failed to converge initial random solutions to the global optimum owing to its complete dependency on the number of iterations for the process of exploration and exploitation. The proposed improved SSA (iSSA) aims to enhance the ability of Salps to explore divergent areas by randomly updating its location. Randomizing the Salps location via Levy flight enriches the exploitation potential of SSA resulting in it converging the model toward the global optima. The performance of the proposed iSSA is investigated using six different high-dimensional microarray datasets. While comparing the ability to converge, it is understood that the proposed model outperforms SSA providing 0.1033% more confidence in the selected features. The results of the simulation revealed that the iSSA can provide better competitive and significant results compared to SSA. [ABSTRACT FROM AUTHOR]

Multiple instance learning (MIL) is a binary classification problem with loosely supervised data where a class label is assigned only to a bag of instances indicating presence/absence of positive instances. In this paper we introduce a novel MIL algorithm using Gaussian processes (GP). The bag labeling protocol of the MIL can be effectively modeled by the sigmoid likelihood through the max function over GP latent variables. As the non-continuous max function makes exact GP inference and learning infeasible, we propose two approximations: the soft-max approximation and the introduction of witness indicator variables. Compared to the state-of-the-art MIL approaches, especially those based on the Support Vector Machine, our model enjoys two most crucial benefits: (i) the kernel parameters can be learned in a principled manner, thus avoiding grid search and being able to exploit a variety of kernel families with complex forms, and (ii) the efficient gradient search for kernel parameter learning effectively leads to feature selection to extract most relevant features while discarding noise. We demonstrate that our approaches attain superior or comparable performance to existing methods on several real-world MIL datasets including large-scale content-based image retrieval problems. [ABSTRACT FROM AUTHOR]

Multilingual text processing is useful because the information content found in different languages is complementary, both regarding facts and opinions. While Information Extraction and other text mining software can, in principle, be developed for many languages, most text analysis tools have only been applied to small sets of languages because the development effort per language is large. Self-training tools obviously alleviate the problem, but even the effort of providing training data and of manually tuning the results is usually considerable. In this paper, we gather insights by various multilingual system developers on how to minimise the effort of developing natural language processing applications for many languages. We also explain the main guidelines underlying our own effort to develop complex text mining software for tens of languages. While these guidelines-most of all: extreme simplicity-can be very restrictive and limiting, we believe to have shown the feasibility of the approach through the development of the Europe Media Monitor (EMM) family of applications (). EMM is a set of complex media monitoring tools that process and analyse up to 100,000 online news articles per day in between twenty and fifty languages. We will also touch upon the kind of language resources that would make it easier for all to develop highly multilingual text mining applications. We will argue that-to achieve this-the most needed resources would be freely available, simple, parallel and uniform multilingual dictionaries, corpora and software tools. [ABSTRACT FROM AUTHOR]

The problem of real-time extraction of meaningful patterns from time-changing data streams is of increasing importance for the machine learning and data mining communities. Regression in time-changing data streams is a relatively unexplored topic, despite the apparent applications. This paper proposes an efficient and incremental stream mining algorithm which is able to learn regression and model trees from possibly unbounded, high-speed and time-changing data streams. The algorithm is evaluated extensively in a variety of settings involving artificial and real data. To the best of our knowledge there is no other general purpose algorithm for incremental learning regression/model trees able to perform explicit change detection and informed adaptation. The algorithm performs online and in real-time, observes each example only once at the speed of arrival, and maintains at any-time a ready-to-use model tree. The tree leaves contain linear models induced online from the examples assigned to them, a process with low complexity. The algorithm has mechanisms for drift detection and model adaptation, which enable it to maintain accurate and updated regression models at any time. The drift detection mechanism exploits the structure of the tree in the process of local change detection. As a response to local drift, the algorithm is able to update the tree structure only locally. This approach improves the any-time performance and greatly reduces the costs of adaptation. [ABSTRACT FROM AUTHOR]

Within the framework of Bayesian networks (BNs), most classifiers assume that the variables involved are of a discrete nature, but this assumption rarely holds in real problems. Despite the loss of information discretization entails, it is a direct easy-to-use mechanism that can offer some benefits: sometimes discretization improves the run time for certain algorithms; it provides a reduction in the value set and then a reduction in the noise which might be present in the data; in other cases, there are some Bayesian methods that can only deal with discrete variables. Hence, even though there are many ways to deal with continuous variables other than discretization, it is still commonly used. This paper presents a study of the impact of using different discretization strategies on a set of representative BN classifiers, with a significant sample consisting of 26 datasets. For this comparison, we have chosen Naive Bayes (NB) together with several other semi-Naive Bayes classifiers: Tree-Augmented Naive Bayes (TAN), k-Dependence Bayesian (KDB), Aggregating One-Dependence Estimators (AODE) and Hybrid AODE (HAODE). Also, we have included an augmented Bayesian network created by using a hill climbing algorithm (BNHC). With this comparison we analyse to what extent the type of discretization method affects classifier performance in terms of accuracy and bias-variance discretization. Our main conclusion is that even if a discretization method produces different results for a particular dataset, it does not really have an effect when classifiers are being compared. That is, given a set of datasets, accuracy values might vary but the classifier ranking is generally maintained. This is a very useful outcome, assuming that the type of discretization applied is not decisive future experiments can be d times faster, d being the number of discretization methods considered. [ABSTRACT FROM AUTHOR]

It is difficult to find the optimal sparse solution of a manifold learning based dimensionality reduction algorithm. The lasso or the elastic net penalized manifold learning based dimensionality reduction is not directly a lasso penalized least square problem and thus the least angle regression (LARS) (Efron et al., Ann Stat 32(2):407-499, 2004), one of the most popular algorithms in sparse learning, cannot be applied. Therefore, most current approaches take indirect ways or have strict settings, which can be inconvenient for applications. In this paper, we proposed the manifold elastic net or MEN for short. MEN incorporates the merits of both the manifold learning based dimensionality reduction and the sparse learning based dimensionality reduction. By using a series of equivalent transformations, we show MEN is equivalent to the lasso penalized least square problem and thus LARS is adopted to obtain the optimal sparse solution of MEN. In particular, MEN has the following advantages for subsequent classification: (1) the local geometry of samples is well preserved for low dimensional data representation, (2) both the margin maximization and the classification error minimization are considered for sparse projection calculation, (3) the projection matrix of MEN improves the parsimony in computation, (4) the elastic net penalty reduces the over-fitting problem, and (5) the projection matrix of MEN can be interpreted psychologically and physiologically. Experimental evidence on face recognition over various popular datasets suggests that MEN is superior to top level dimensionality reduction algorithms. [ABSTRACT FROM AUTHOR]

The support vector machine (SVM) is a novel machine learning tool in data mining. In this paper, the geometric approach based on the compressed convex hull (CCH) with a mathematical framework is introduced to solve SVM classification problems. Compared with the reduced convex hull (RCH), CCH preserves the shape of geometric solids for data sets; meanwhile, it is easy to give the necessary and sufficient condition for determining its extreme points. As practical applications of CCH, spare and probabilistic speed-up geometric algorithms are developed. Results of numerical experiments show that the proposed algorithms can reduce kernel calculations and display nice performances. [ABSTRACT FROM AUTHOR]

To cleanse mislabeled examples from a training dataset for efficient and effective induction, most existing approaches adopt a major set oriented scheme: the training dataset is separated into two parts (a major set and a minor set). The classifiers learned from the major set are used to identify noise in the minor set. The obvious drawbacks of such a scheme are twofold: (1) when the underlying data volume keeps growing, it would be either physically impossible or lime consuming to load the major set into the memory for inductive learning; and (2) for multiple or distributed datasets, it can be either technically infeasible or factitiously forbidden to download data from other sites (for security or privacy reasons). Therefore, these approaches have severe limitations in conducting effective global data cleansing from large, distributed datasets. In this paper, we propose a solution to bridge the local and global analysis for noise cleansing. More specifically, the proposed effort tries to identify and eliminate mislabeled data items from large or distributed datasets through local analysis and global incorporation. For this purpose, we make use of distributed datasets or partition a large dataset into subsets, each of which is regarded as a local subset and is small enough to be processed by an induction algorithm at one time to construct a local model for noise identification. We construct good rules from each subset, and use the good rules to evaluate the whole dataset. For a given instance Ik, two error count variables are used to count the number of times it has been identified as noise by all data subsets. The instance with higher error values will have a higher probability of being a mislabeled example. Two threshold schemes, majority and non-objection, are used to identify and eliminate the noisy examples. Experimental results and comparative studies on both real-world and synthetic datasets are reported to evaluate the effectiveness and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

A correct protocol assignment is critical to high-quality imaging examinations, and its automation can be amenable to natural language processing (NLP). Assigning protocols for abdominal imaging CT scans is particularly challenging given the multiple organ specific indications and parameters. We compared conventional machine learning, deep learning, and automated machine learning builder workflows for this multiclass text classification task. A total of 94,501 CT studies performed over 4 years and their assigned protocols were obtained. Text data associated with each study including the ordering provider generated free text study indication and ICD codes were used for NLP analysis and protocol class prediction. The data was classified into one of 11 abdominal CT protocol classes before and after augmentations used to account for imbalances in the class sample sizes. Four machine learning (ML) algorithms, one deep learning algorithm, and an automated machine learning (AutoML) builder were used for the multilabel classification task: Random Forest (RF), Tree Ensemble (TE), Gradient Boosted Tree (GBT), multi-layer perceptron (MLP), Universal Language Model Fine-tuning (ULMFiT), and Google's AutoML builder (Alphabet, Inc., Mountain View, CA), respectively. On the unbalanced dataset, the manually coded algorithms all performed similarly with F1 scores of 0.811 for RF, 0.813 for TE, 0.813 for GBT, 0.828 for MLP, and 0.847 for ULMFiT. The AutoML builder performed better with a F1 score of 0.854. On the balanced dataset, the tree ensemble machine learning algorithm performed the best with an F1 score of 0.803 and a Cohen's kappa of 0.612. AutoML methods took a longer time for completion of NLP model training and evaluation, 4 h and 45 min compared to an average of 51 min for manual methods. Machine learning and natural language processing can be used for the complex multiclass classification task of abdominal imaging CT scan protocol assignment. [ABSTRACT FROM AUTHOR]

A multiple clustering algorithm based on high-dimensional automatic identification system (AIS) data is proposed to extract the important waypoints in the ship's navigation trajectory based on selected AIS attribute features and construct a route network using the waypoints. The algorithm improves the accuracy of route network planning by using the latitude and longitude of the historical voyage trajectory and the heading to the ground. Unlike the navigation clustering method that only uses ship latitude and longitude coordinates, the algorithm first calculates the major waypoints using Clustering in QUEst (CLIQUE) and Balance Iterative Reducing and Clustering Using Hierarchies (BIRCH) algorithms, and then builds the route network using network construction. Under the common PC specification (i5 processor), this algorithm forms 440 major waypoints from 220,133 AIS data and constructs a route network with directional features in 5 min, which is faster in computing speed and more suitable for complex ship trajectory differentiation and can extend the application boundary of ship route planning. [ABSTRACT FROM AUTHOR]

We study the problem of simplifying a given directed graph by keeping a small subset of its arcs. Our goal is to maintain the connectivity required to explain a set of observed traces of information propagation across the graph. Unlike previous work, we do not make any assumption about an underlying model of information propagation. Instead, we approach the task as a combinatorial problem. We prove that the resulting optimization problem is $$\mathbf{NP}$$ -hard. We show that a standard greedy algorithm performs very well in practice, even though it does not have theoretical guarantees. Additionally, if the activity traces have a tree structure, we show that the objective function is supermodular, and experimentally verify that the approach for size-constrained submodular minimization recently proposed by Nagano et al. (28th International Conference on Machine Learning, ) produces very good results. Moreover, when applied to the task of reconstructing an unobserved graph, our methods perform comparably to a state-of-the-art algorithm devised specifically for this task. [ABSTRACT FROM AUTHOR]

We consider the problem of clustering a set of points so as to minimize the maximum intra-cluster dissimilarity, which is strongly NP-hard. Exact algorithms for this problem can handle datasets containing up to a few thousand observations, largely insufficient for the nowadays needs. The most popular heuristic for this problem, the complete-linkage hierarchical algorithm, provides feasible solutions that are usually far from optimal. We introduce a sampling-based exact algorithm aimed at solving large-sized datasets. The algorithm alternates between the solution of an exact procedure on a small sample of points, and a heuristic procedure to prove the optimality of the current solution. Our computational experience shows that our algorithm is capable of solving to optimality problems containing more than 500,000 observations within moderate time limits, this is two orders of magnitude larger than the limits of previous exact methods. [ABSTRACT FROM AUTHOR]

Today, it is common to include machine learning components in software products. These components offer specific functionalities such as image recognition, time series analysis, and forecasting but may not satisfy the non-functional constraints of the software products. It is difficult to identify suitable learning algorithms for a particular task and software product because the non-functional requirements of the product affect algorithm suitability. A particular suitability evaluation may thus require the assessment of multiple criteria to analyse trade-offs between functional and non-functional requirements. For this purpose, we present a method for APPlication-Oriented Validation and Evaluation (APPrOVE). This method comprises four sequential steps that address the stated evaluation problem. The method provides a common ground for different stakeholders and enables a multi-expert and multi-criteria evaluation of machine learning algorithms prior to inclusion in software products. Essentially, the problem addressed in this article concerns how to choose the appropriate machine learning component for a particular software product. [ABSTRACT FROM AUTHOR]

We consider an automated agent that needs to coordinate with a human partner when communication between them is not possible or is undesirable ( tacit coordination games). Specifically, we examine situations where an agent and human attempt to coordinate their choices among several alternatives with equivalent utilities. We use machine learning algorithms to help the agent predict human choices in these tacit coordination domains. Experiments have shown that humans are often able to coordinate with one another in communication-free games, by using focal points, 'prominent' solutions to coordination problems. We integrate focal point rules into the machine learning process, by transforming raw domain data into a new hypothesis space. We present extensive empirical results from three different tacit coordination domains. The Focal Point Learning approach results in classifiers with a 40-80% higher correct classification rate, and shorter training time, than when using regular classifiers, and a 35% higher correct classification rate than classical focal point techniques without learning. In addition, the integration of focal points into learning algorithms results in agents that are more robust to changes in the environment. We also present several results describing various biases that might arise in Focal Point based coordination. [ABSTRACT FROM AUTHOR]

Effective information systems require the existence of explicit process models. A completely specified process design needs to be developed in order to enact a given business process. This development is time consuming and often subjective and incomplete. We propose a method that constructs the process model from process log data, by determining the relations between process tasks. To predict these relations, we employ machine learning technique to induce rule sets. These rule sets are induced from simulated process log data generated by varying process characteristics such as noise and log size. Tests reveal that the induced rule sets have a high predictive accuracy on new data. The effects of noise and imbalance of execution priorities during the discovery of the relations between process tasks are also discussed. Knowing the causal, exclusive, and parallel relations, a process model expressed in the Petri net formalism can be built. We illustrate our approach with real world data in a case study. [ABSTRACT FROM AUTHOR]

Protein domains are the structural and functional units of proteins. The ability to parse protein chains into different domains is important for protein classification and for understanding protein structure, function, and evolution. Here we use machine learning algorithms, in the form of recursive neural networks, to develop a protein domain predictor called DOMpro. DOMpro predicts protein domains using a combination of evolutionary information in the form of profiles, predicted secondary structure, and predicted relative solvent accessibility. DOMpro is trained and tested on a curated dataset derived from the CATH database. DOMpro correctly predicts the number of domains for 69% of the combined dataset of single and multi-domain chains. DOMpro achieves a sensitivity of 76% and specificity of 85% with respect to the single-domain proteins and sensitivity of 59% and specificity of 38% with respect to the two-domain proteins. DOMpro also achieved a sensitivity and specificity of 71% and 71% respectively in the Critical Assessment of Fully Automated Structure Prediction 4 (CAFASP-4) (Fisher et al., 1999; Saini and Fischer, 2005) and was ranked among the top ab initio domain predictors. The DOMpro server, software, and dataset are available at http://www.igb.uci.edu/servers/psss.html. [ABSTRACT FROM AUTHOR]