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Virtual machine (VM) consolidation and migration that only consider current workload can result in excessive unnecessary migrations. To address this issue, a VM consolidation algorithm based on resource utilization prediction is proposed. An improved K-nearest neighbor (KNN) classification algorithm weighted by attribute inconsistency is proposed to predict the workload of both the host and the VMs. Firstly, two distributions are partitioned according to the neighboring relationship for comparing consistency. Then, an inconsistency evaluation function based on earth mover's distance (EMD) is designed to measure the inconsistency between the neighboring sample set of each sample under each attribute and the equivalent partition refined by the decision attribute. Finally, the inconsistency level of the neighboring samples is transformed into the importance of the corresponding attribute to implement the attribute weighting KNN classifier. When selecting the source host and target host for VM migration, both current and predicted overloads are considered to avoid unnecessary VM migrations. Simulation tests were performed with random and realistic workloads, and the results show that the proposed method can reduce the overall energy consumption of the host, while also reducing service level agreement (SLA) violations and VM migration. [ABSTRACT FROM AUTHOR]

Aiming at the problem that attributes in the existing neighborhood rough set model all have the same weight, which cannot ensure that the key attributes can be retained in attribute approximation, this paper proposed an attribute approximation algorithm based on information entropy weighting. Firstly, it adopted interclass entropy and intraclass entropy strategies to assign weights to attributes based on the principle of maximising interclass entropy and minimising intraclass entropy. Secondly, it constructed a weighted neighborhood rough set model based on weighted neighborhood relationships. Finally, it assessed the importance of attribute subsets based on dependency relationships to achieve attribute simplification. Comparison experiments with other three attribute approximation algorithms on UCI-based dataset show that the proposed algorithm can effectively remove redundancy and improve classification accuracy. [ABSTRACT FROM AUTHOR]

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In person attribute recognition (PAR), an individual is described by his or her appearance. PAR-based person retrieval is a cross-modal problem where the input is a textual description of the person's appearance and the output is an image of the person. The paper introduces PAR model development by merging a large-scale RAP dataset with the person retrieval benchmark dataset of AVSS 2018 challenge II. It uses a single deep network to detect a person's attributes. The proposed approach uses five attributes; age, upper body (uBody) clothing color, uBody clothing type, lower body (lBody) clothing color, and lBody clothing type. Mask R-CNN is used for person detection, and the approach weighs each attribute to generate a ranking score for every detected person. Unlike the existing approaches, the proposed method uses a single deep network and fewer attributes to achieve state-of-the-art average intersection-of-union (IoU) of 66.7%, retrieval with IoU ≥ 0.4 is 85.6%, and an average true positive rate (TPR) of 85.30%. It is better by 10.80% average IoU, 5.94% IoU ≥ 0.4, and 3.85% TPR than the existing state-of-the-art person retrieval using attributes recognition. [ABSTRACT FROM AUTHOR]

Mixed data containing categorical and numerical attributes are widely available in real-world. Before analysing such data, it is typically necessary to process (transform/embed/represent) them into high-quality numerical data. The conditional probability transformation method (CPT) can provide acceptable performance in the majority of cases, but it is not satisfactory for datasets with strong attribute association. Inspired by the one dependence value difference metric method, the concept of relaxing the attributes conditional independence has been applied to CPT, but this approach has the drawback of dramatically-expanding the attribute dimensionality. We employ the isometric embedding method to tackle the problem of dimensionality expansion. In addition, an attribute weighting method based on the must-link and cannot-link constraints is designed to optimize the data transformation quality. Combining these methods, we propose an attribute-weighted isometric embedding (AWIE) for categorical encoding on mixed data. Extensive experimental results obtained on 16 datasets demonstrate that AWIE significantly improves upon the classification performance (increasing the F1-score by 2.54%, attaining 6/16 best results, and reaching average ranks of 1.94/8), compared with 28 competitors. [ABSTRACT FROM AUTHOR]

Functionally characterized lncRNAs play critical roles in cancer progression but the potential relationship between lncRNAs and herbal medicine is yet to be known. To identify this association by RNA‐seq data for breast and prostate cancer, a co‐expression network in response to herbal medicines was performed. GO terms and pathway analyses on differential co‐expressed mRNAs revealed that lncRNAs were widely co‐expressed with metabolic process genes. On the other hand, various machine learning‐based prediction systems on the differential co‐expressed lncRNAs were implemented. Results show that the Deep Learning model could accurately forecast cancer‐related lncRNAs. Background: Accumulating evidence shows that long non‐coding RNAs (lncRNAs) play critical roles in cancer progression. The possible association between lncRNAs and herbal medicine is yet to be known. This study aims to identify medicinal herbs associated with lncRNAs by RNA‐seq data for breast and prostate cancer. Methods: To develop the optimal approach for identifying cancer‐related lncRNAs, we implemented two steps: (1) applying protein–protein interaction (PPI), Gene Ontology (GO), and pathway analyses, and (2) applying attribute weighting and finding the efficient classification model of the machine learning approach. Results: In the first step, GO terms and pathway analyses on differential co‐expressed mRNAs revealed that lncRNAs were widely co‐expressed with metabolic process genes. We identified two hub lncRNA‐mRNA networks that implicate lncRNAs associated with breast and prostate cancer. In the second step, we implemented various machine learning‐based prediction systems (Decision Tree, Random Forest, Deep Learning, and Gradient‐Boosted Tree) on the non‐transformed and Z‐standardized differential co‐expressed lncRNAs. Based on five‐fold cross‐validation, we obtained high accuracy (91.11%), high sensitivity (88.33%), and high specificity (93.33%) in Deep Learning which reinforces the biomarker power of identified lncRNAs in this study. As data originally came from different cell lines at different durations of herbal treatment intervention, we applied seven attribute weighting algorithms to check the effects of variables on identifying lncRNAs. Attribute weighting results showed that the cell line and time had little or no effect on the selected lncRNAs list. Besides, we identified one known lncRNAs, downregulated RNA in cancer (DRAIC), as an essential feature. Conclusions: This study will provide further insights to investigate the potential therapeutic and prognostic targets for prostate cancer (PC) and breast cancer (BC) in common. [ABSTRACT FROM AUTHOR]

Naïve Bayes (NB) classification performance degrades if the conditional independence assumption is not satisfied or if the conditional probability estimate is not realistic due to the attributes of correlation and scarce data, respectively. Many works address these two problems, but few works tackle them simultaneously. Existing methods heuristically employ information theory or applied gradient optimization to enhance NB classification performance, however, to the best of our knowledge, the enhanced model generalization capability deteriorated especially on scant data. In this work, we propose a fine-grained boosting of the NB classifier to identify hidden and potential discriminative attribute values that lead the NB model to underfit or overfit on the training data and to enhance their predictive power. We employ the complement harmonic average of the conditional probability terms to measure their distribution divergence and impact on the classification performance for each attribute value. The proposed method is subtle yet significant enough in capturing the attribute values' inter-correlation (between classes) and intra-correlation (within the class) and elegantly and effectively measuring their impact on the model's performance. We compare our proposed complement-class harmonized Naïve Bayes classifier (CHNB) with the state-of-the-art Naive Bayes and imbalanced ensemble boosting methods on general and imbalanced machine-learning benchmark datasets, respectively. The empirical results demonstrate that CHNB significantly outperforms the compared methods. [ABSTRACT FROM AUTHOR]

Distance metrics are central to many machine learning algorithms. Improving their measurement performance can greatly affect the classification result of these algorithms. The inverted specific-class distance measure (ISCDM) is effective in handling nominal attributes rather than numeric ones, especially if a training set contains missing values and non-class attribute noise. However, similar to many other distance metrics, this method is still based on the attribute independence assumption, which is obviously infeasible for many real-world datasets. In this study, we focus on establishing an improved ISCDM by using an attribute weighting scheme to address its attribute independence assumption. We use a differential evolution (DE) algorithm to determine better attribute weights for our improved ISCDM, which is thus denoted as DE-AWISCDM. We experimentally tested our DE-AWISCDM on 29 UCI datasets, and find that it significantly outperforms the original ISCDM and other state-of-the-art methods with respect to negative conditional log likelihood and root relative squared error. [ABSTRACT FROM AUTHOR]

Numerous enhancements have been proposed to mitigate the attribute conditional independence assumption in naive Bayes (NB). However, almost all of them only focus on the original attribute space. Due to the complexity of real-world applications, we argue that the discriminative information provided by the original attribute space might be insufficient for classification. Thus, in this study, we expect to discover some latent attributes beyond the original attribute space and propose a novel two-stage model called attribute augmented and weighted naive Bayes (A2WNB). At the first stage, we build multiple random one-dependence estimators (RODEs). Then we use each built RODE to classify each training instance in turn and define the predicted class labels as its latent attributes. At last, we construct the augmented attributes by concatenating the latent attributes with the original attributes. At the second stage, to alleviate the attribute redundancy, we optimize the augmented attributes’ weights by maximizing the conditional log-likelihood (CLL) of the built model. Extensive experimental results show that A2WNB significantly outperforms NB and all the other existing state-of-the-art competitors. [ABSTRACT FROM AUTHOR]

The naive Bayesian classification method has received significant attention in the field of supervised learning. This method has an unrealistic assumption in that it views all attributes as equally important. Attribute weighting is one of the methods used to alleviate this assumption and consequently improve the performance of the naive Bayes classification. This study, with a focus on nonlinear optimization problems, proposes four attribute weighting methods by minimizing four different loss functions. The proposed loss functions belong to a family of exponential functions that makes the optimization problems more straightforward to solve, provides analytical properties of the trained classifier, and allows for the simple modification of the loss function such that the naive Bayes classifier becomes robust to noisy instances. This research begins with a typical exponential loss which is sensitive to noise and provides a series of its modifications to make naive Bayes classifiers more robust to noisy instances. Based on numerical experiments conducted using 28 datasets from the UCI machine learning repository, we confirmed that the proposed scheme successfully determines optimal attribute weights and improves the classification performance.

Methods and techniques of feature selection support expert domain knowledge in the search for attributes, which are the most important for a task. These approaches can also be used in the process of closer tailoring of the obtained solutions when dimensionality reduction is aimed not only at variables but also at learners. The paper reports on research where attribute rankings were employed to filter induced decision rules. The rankings were constructed through the proposed weighting factor based on the concept of decision reducts—a feature reduction mechanism embedded in the rough set theory. Classical rough sets operate only in discrete input space by indiscernibility relation. Replacing it with dominance enables processing real-valued data. Decision reducts were found for both numeric and discrete attributes, transformed by selected discretisation approaches. The calculated ranking scores were used to control the selection of decision rules. The performance of the resulting rule classifiers was observed for the entire range of rejected variables, for decision rules with conditions on continuous values, discretised conditions, and also inferred from discrete data. The predictive powers were analysed and compared to detect existing trends. The experiments show that for all variants of the rule sets, not only was dimensionality reduction possible, but also predictions were improved, which validated the proposed methodology. [ABSTRACT FROM AUTHOR]

Naive Bayes (NB) is one of the essential algorithms in data mining. However, it is rarely used in reality because of the attribute independence assumption. Researchers have proposed many improved NB methods to alleviate this assumption. Among these methods, due to its high efficiency and easy implementation, the filter-attribute-weighted NB methods have received great attentions. However, there still exist several challenges, such as the poor representation ability for a single index and the fusion problem of two indexes. To overcome the above challenges, we propose a general framework of an adaptive two-index fusion attribute-weighted NB (ATFNB). Two types of data description category are used to represent the correlation between classes and attributes, the intercorrelation between attributes and attributes, respectively. ATFNB can select any one index from each category. Then, we introduce a regulatory factor β to fuse two indexes, which can adaptively adjust the optimal ratio of any two indexes on various datasets. Furthermore, a range query method is proposed to infer the optimal interval of regulatory factor β. Finally, the weight of each attribute is calculated using the optimal value β and is integrated into an NB classifier to improve the accuracy. The experimental results on 50 benchmark datasets and a Flavia dataset show that ATFNB outperforms the basic NB and state-of-the-art filter-weighted NB models. In addition, the ATFNB framework can improve the existing two-index NB model by introducing the adaptive regulatory factor β. Auxiliary experimental results demonstrate the improved model significantly increases the accuracy compared to the original model without the adaptive regulatory factor β. [ABSTRACT FROM AUTHOR]

Naive Bayes (NB) is one of the top 10 data mining algorithms due to its simplicity, efficiency and efficacy. However, both the unrealistic attribute conditional independence assumption and the unreliable conditional probability estimation limit its performance. Of numerous improved approaches, attribute weighting only focuses on alleviating the unrealistic attribute conditional independence assumption, while fine tuning devotes all the efforts to finding a more reliable conditional probability estimation. In this study, we argue that both of them are equally important to enhance the performance of NB and propose a novel model called fine tuned attribute weighted NB (FTAWNB) by combining fine tuning with attribute weighting into a uniform framework. In FTAWNB, we first exploit correlation-based attribute weighting to initialize the conditional probabilities, then for each misclassified training instance, the conditional probabilities are fine tuned iteratively to make them more reliable, and the fine tuning process will stop once the training classification accuracy no longer improves. Extensive experimental results show that FTAWNB significantly outperforms all the other existing state-of-the-art competitors. [ABSTRACT FROM AUTHOR]

Naïve Bayes (NB) classification performance degrades if the conditional independence assumption is not satisfied or if the conditional probability estimate is not realistic due to the attributes of correlation and scarce data, respectively. Many works address these two problems, but few works tackle them simultaneously. Existing methods heuristically employ information theory or applied gradient optimization to enhance NB classification performance, however, to the best of our knowledge, the enhanced model generalization capability deteriorated especially on scant data. In this work, we propose a fine-grained boosting of the NB classifier to identify hidden and potential discriminative attribute values that lead the NB model to underfit or overfit on the training data and to enhance their predictive power. We employ the complement harmonic average of the conditional probability terms to measure their distribution divergence and impact on the classification performance for each attribute value. The proposed method is subtle yet significant enough in capturing the attribute values’ inter-correlation (between classes) and intra-correlation (within the class) and elegantly and effectively measuring their impact on the model’s performance. We compare our proposed complement-class harmonized Naïve Bayes classifier (CHNB) with the state-of-the-art Naive Bayes and imbalanced ensemble boosting methods on general and imbalanced machine-learning benchmark datasets, respectively. The empirical results demonstrate that CHNB significantly outperforms the compared methods.

The naïve Bayes classifier is one of the commonly used data mining methods for classification. Despite its simplicity, naïve Bayes is effective and computationally efficient. Although the strong attribute independence assumption in the naïve Bayes classifier makes it a tractable method for learning, this assumption may not hold in real-world applications. Many enhancements to the basic algorithm have been proposed in order to alleviate the violation of attribute independence assumption. While these methods improve the classification performance, they do not necessarily retain the mathematical structure of the naïve Bayes model and some at the expense of computational time. One approach to reduce the naïveté of the classifier is to incorporate attribute weights in the conditional probability. In this paper, we proposed a method to incorporate attribute weights to naïve Bayes. To evaluate the performance of our method, we used the public benchmark datasets. We compared our method with the standard naïve Bayes and baseline attribute weighting methods. Experimental results show that our method to incorporate attribute weights improves the classification performance compared to both standard naïve Bayes and baseline attribute weighting methods in terms of classification accuracy and F1, especially when the independence assumption is strongly violated, which was validated using the Chi-square test of independence. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative method to improve the attribute weighting approaches for naïve Bayes text classifiers using the improved distance correlation coefficient. The resulted model is called improved distance correlation coefficient attribute weighted multinomial naïve Bayes, denoted by IDCWMNB. Unlike the traditional correlation statistical measurements that consider the cumulative distribution function of random vectors, the improved distance correlation coefficient tests the joint correlation of random vectors by describing the distance between the joint characteristic function and the product of the marginal characteristic functions. Specifically, a measurement of inverse document frequency that considers the distribution information of document concentrating and scattering has been proposed. Then, the measurement and the distance correlation coefficient between attributes and categories have been combined to measure the importance of attributes to categories, to allocate different weights to different terms. Meanwhile, the learned attribute weights are incorporated into the posterior probability estimates of the multinomial naïve Bayes model, which is known as deep attribute weighting. This measurement is more effective than the traditional statistical measurements in the presence of nonlinear relationship between two random vectors. Experimental results taking benchmark and real-world data indicate that the new attribute weighting method can achieve an effective balance between classification accuracy and execution time. [ABSTRACT FROM AUTHOR]

K-nearest neighbor (KNN) rule is a very simple and efficient non-parametric classification algorithm that is widely used in machine learning. In this paper, we proposed a attribute weighting local-mean pseudo nearest neighbor rule (AWLMPNN). The main difference of AWLMPNN and local mean-based pseudo nearest neighbor (LMPNN) is that they use attribute weighting distance and Euclidean distance to measure the distance between two samples, respectively. To illustrate the effectiveness of the proposed AWLMPNN method, extensive experiments on 30 real UCI data sets are conduced by comparing with four competing KNN-based methods. The experimental results show that the proposed AWLMPNN method is superior to other methods, especially in the case of high dimensional attributes with small sample size. [ABSTRACT FROM AUTHOR]

Naive Bayes (NB) was once awarded as one of the top 10 data mining algorithms, but the unreliable probability estimation and the unrealistic attribute conditional independence assumption limit its performance. To alleviate these two primary weaknesses simultaneously, instance and attribute weighting has been recently proposed. However, the existing approach learns instance and attribute weights separately, without considering their interactions at all, which restricts the performance of the learned model. Therefore, in this study, we propose a novel approach to learning instance and attribute weights collaboratively and call the resulting model collaboratively weighted naive Bayes (CWNB). In CWNB, we first learn the weight of each training instance iteratively based on its estimated posterior probability loss to make the prior and conditional probabilities more accurate, then we incorporate these two probabilities into the conditional log-likelihood (CLL) formula, and at last we search the optimal weight of each attribute by maximizing the CLL. Extensive experimental results show that CWNB significantly outperforms the standard NB and all the other existing state-of-the-art competitors. [ABSTRACT FROM AUTHOR]

kNN adalah salah satu metode yang popular karena mudah dieksploitasi, generalisasi yang biak, mudah dimengerti, kemampuan beradaptasi ke ruang fitur yang rumit, intuitif, atraktif, efektif, flexibility, mudah diterapkan, sederhana dan memiliki hasil akurasi yang cukup baik. Namun kNN memiliki beberapa kelemahan, diantaranya memberikan bobot yang sama pada setiap attribut sehingga attribut yang tidak relevant juga memberikan dampak yang sama dengan attribut yang relevant terhadap kemiripan antar data. Masalah lain dari kNN adalah pemilihan tetangga terdekat dengan system suara terbanyak, dimana system ini mengabaikan kemiripan setiap tetangga terdekat dan kemungkinan munculnya mayoritas ganda serta kemungkinan terpilihnya outlier sebagai tetangga terdekat. Masalah-masalah tersebut tentu saja dapat menimbulkan kesalahan klasifikasi yang mengakibatkan rendahnya akurasi. Pada penelitian kali ini akan dilakukan peningkatan akurasi dari kNN tersebut dalam melakukan klasifikasi terhadap data Index Standar Pencemaran Udara di Pekanbaru dengan menggunakan pembobotan attribut (Attibute Weighting) dan local mean. Adapun hasil dari penelitian ini didapati bahwa metode yang diusulkan mampu untuk meningkatkan akurasi sebesar 2.42% dengan rata-rata tingkat akurasi sebesar 97.09%.

Naïve Bayes (NB) algorithm is still in the top ten of the Data Mining algorithms because of it is simplicity, efficiency, and performance. To handle classification on numerical data, the Gaussian distribution and kernel approach can be applied to NB (GNB and KNB). However, in the process of NB classifying, attributes are considered independent, even though the assumption is not always right in many cases. Absolute Correlation Coefficient can determine correlations between attributes and work on numerical attributes, so that it can be applied for attribute weighting to GNB (ACW-NB). Furthermore, because performance of NB does not increase in large datasets, so ACW-NB can be a classifier in the local learning model, where other classification methods, such as K-Nearest Neighbor (K-NN) which are very well known in local learning can be used to obtain sub-dataset in the ACW-NB training. To reduction of noise/bias, then missing value replacement and data normalization can also be applied. This proposed method is termed "LL-KNN ACW-NB (Local Learning K-Nearest Neighbor in Absolute Correlation Weighted Naïve Bayes)," with the objective to improve the performance of NB (GNB and KNB) in handling classification on numerical data. The results of this study indicate that the LL-KNN ACW-NB is able to improve the performance of NB, with an average accuracy of 91,48%, 1,92% better than GNB and 2,86% better than KNB.

Naive Bayes (NB) is an extremely simple and remarkably effective approach to classification learning, but its conditional independence assumption rarely holds true in real-world applications. Attribute weighting is known as a flexible model via assigning each attribute a different weight discriminatively to improve NB. Attribute weighting approaches can fall into two broad categories: filters and wrappers. Wrappers receive a bigger boost in terms of classification accuracy compared with filters, but the time complexity of wrappers is much higher than filters. In order to improve the time complexity of a wrapper, a filter can be used to optimize the initial weight of all attributes as a preprocessing step. So a hybrid attribute weighting approach is proposed in this paper, and the improved model is called correlation-based weight adjusted naive Bayes (CWANB). In CWANB, the correlation-based attribute weighting filter is used to initialize the attribute weights, and then each weight is optimized by the attribute weight adjustment wrapper where the objective function is designed based on dynamic adjustment of attribute weights. Extensive experimental results show that CWANB outperforms NB and some other existing state-of-the-art attribute weighting approaches in terms of the classification accuracy. Meanwhile, compared with the existing wrapper, the CWANB approach reduces the time complexity dramatically.

The Bayesian classification framework has been widely used in many fields, but the covariance matrix is usually difficult to estimate reliably. To alleviate the problem, many naive Bayes (NB) approaches with good performance have been developed. However, the assumption of conditional independence between attributes in NB rarely holds in reality. Various attribute-weighting schemes have been developed to address this problem. Among them, class-specific attribute weighted naive Bayes (CAWNB) has recently achieved good performance by using classification feedback to optimize the attribute weights of each class. However, the derived model may be over-fitted to the training dataset, especially when the dataset is insufficient to train a model with good generalization performance. This paper proposes a regularization technique to improve the generalization capability of CAWNB, which could well balance the trade-off between discrimination power and generalization capability. More specifically, by introducing the regularization term, the proposed method, namely regularized naive Bayes (RNB), could well capture the data characteristics when the dataset is large, and exhibit good generalization performance when the dataset is small. RNB is compared with the state-of-the-art naive Bayes methods. Experiments on 33 machine-learning benchmark datasets demonstrate that RNB outperforms the compared methods significantly.

Computational models and simulations often involve representations of decision-making processes. Numerous methods exist for representing decision-making at varied resolution levels based on the objectives of the simulation and the desired level of fidelity for validation. Decision making relies on the type of decision and the criteria that is appropriate for making the decision; therefore, decision makers can reach unique decisions that meet their own needs given the same information. Accounting for personalized weighting scales can help to reflect a more realistic state for a modeled system. To this end, this article reviews and summarizes eight multi-criteria decision analysis (MCDA) techniques that serve as options for reaching unique decisions based on personally and individually ranked criteria. These techniques are organized into a taxonomy of ratio assignment and approximate techniques, and the strengths and limitations of each are explored. We compare these techniques potential uses across the Agent-Based Modeling (ABM), System Dynamics (SD), and Discrete Event Simulation (DES) modeling paradigms to inform current researchers, students, and practitioners on the state-of-the-art and to enable new researchers to utilize methods for modeling multi-criteria decisions. [ABSTRACT FROM AUTHOR]

Background: Mastitis is the most prevalent disease in dairy cattle and one of the most significant bovine pathologies affecting milk production, animal health, and reproduction. In addition, mastitis is the most common, expensive, and contagious infection in the dairy industry. Methods: A meta-analysis of microarray and RNA-seq data was conducted to identify candidate genes and functional modules associated with mastitis disease. The results were then applied to systems biology analysis via weighted gene coexpression network analysis (WGCNA), Gene Ontology, enrichment analysis for the Kyoto Encyclopedia of Genes and Genomes (KEGG), and modeling using machine-learning algorithms. Results: Microarray and RNA-seq datasets were generated for 2,089 and 2,794 meta-genes, respectively. Between microarray and RNA-seq datasets, a total of 360 meta-genes were found that were significantly enriched as "peroxisome," "NOD-like receptor signaling pathway," "IL-17 signaling pathway," and "TNF signaling pathway" KEGG pathways. The turquoise module (n = 214 genes) and the brown module (n = 57 genes) were identified as critical functional modules associated with mastitis through WGCNA. PRDX5, RAB5C, ACTN4, SLC25A16, MAPK6, CD53, NCKAP1L, ARHGEF2, COL9A1 , and PTPRC genes were detected as hub genes in identified functional modules. Finally, using attribute weighting and machine-learning methods, hub genes that are sufficiently informative in Escherichia coli mastitis were used to optimize predictive models. The constructed model proposed the optimal approach for the meta-genes and validated several high-ranked genes as biomarkers for E. coli mastitis using the decision tree (DT) method. Conclusion: The candidate genes and pathways proposed in this study may shed new light on the underlying molecular mechanisms of mastitis disease and suggest new approaches for diagnosing and treating E. coli mastitis in dairy cattle. [ABSTRACT FROM AUTHOR]

Background: Mastitis is the most prevalent disease in dairy cattle and one of the most significant bovine pathologies affecting milk production, animal health, and reproduction. In addition, mastitis is the most common, expensive, and contagious infection in the dairy industry.Methods: A meta-analysis of microarray and RNA-seq data was conducted to identify candidate genes and functional modules associated with mastitis disease. The results were then applied to systems biology analysis via weighted gene coexpression network analysis (WGCNA), Gene Ontology, enrichment analysis for the Kyoto Encyclopedia of Genes and Genomes (KEGG), and modeling using machine-learning algorithms.Results: Microarray and RNA-seq datasets were generated for 2,089 and 2,794 meta-genes, respectively. Between microarray and RNA-seq datasets, a total of 360 meta-genes were found that were significantly enriched as “peroxisome,” “NOD-like receptor signaling pathway,” “IL-17 signaling pathway,” and “TNF signaling pathway” KEGG pathways. The turquoise module (n = 214 genes) and the brown module (n = 57 genes) were identified as critical functional modules associated with mastitis through WGCNA. PRDX5, RAB5C, ACTN4, SLC25A16, MAPK6, CD53, NCKAP1L, ARHGEF2, COL9A1, and PTPRC genes were detected as hub genes in identified functional modules. Finally, using attribute weighting and machine-learning methods, hub genes that are sufficiently informative in Escherichia coli mastitis were used to optimize predictive models. The constructed model proposed the optimal approach for the meta-genes and validated several high-ranked genes as biomarkers for E. coli mastitis using the decision tree (DT) method.Conclusion: The candidate genes and pathways proposed in this study may shed new light on the underlying molecular mechanisms of mastitis disease and suggest new approaches for diagnosing and treating E. coli mastitis in dairy cattle.

Methods and techniques of feature selection support expert domain knowledge in the search for attributes, which are the most important for a task. These approaches can also be used in the process of closer tailoring of the obtained solutions when dimensionality reduction is aimed not only at variables but also at learners. The paper reports on research where attribute rankings were employed to filter induced decision rules. The rankings were constructed through the proposed weighting factor based on the concept of decision reducts—a feature reduction mechanism embedded in the rough set theory. Classical rough sets operate only in discrete input space by indiscernibility relation. Replacing it with dominance enables processing real-valued data. Decision reducts were found for both numeric and discrete attributes, transformed by selected discretisation approaches. The calculated ranking scores were used to control the selection of decision rules. The performance of the resulting rule classifiers was observed for the entire range of rejected variables, for decision rules with conditions on continuous values, discretised conditions, and also inferred from discrete data. The predictive powers were analysed and compared to detect existing trends. The experiments show that for all variants of the rule sets, not only was dimensionality reduction possible, but also predictions were improved, which validated the proposed methodology.

At every waking moment, one's mode of attention is situated at some point on a spectrum ranging from experiencing, where attention is directed toward perceptions and cognitions related to the immediate physical environment, to mind-wandering, where attention is directed toward thoughts, feelings, and daydreams that are decoupled from the environment. Across five studies, the authors propose and find that people in an experiencing (vs. mind-wandering) mode place more importance on detecting change in their environment, which leads them to prioritize attention toward changeable stimuli (like price) and subsequently afford such stimuli greater weight in judgments and decisions. The research not only uncovers a novel stimuli characteristic--changeability--important in both the domain of attention modes and judgments but also diverges from the typical characterization of price as a salient cue or heuristic to generate a unique set of findings based on price's inherently changeable nature. More broadly, the findings highlight a way in which consumers' fundamental judgment and decision-making processes are shaped by cognitive mechanisms designed for the physical world. [ABSTRACT FROM AUTHOR]

Attribute weighting is a task of paramount relevance in multi-attribute decision-making (MADM). Over the years, different approaches have been developed to face this problem. Despite the effort of the community, there is a lack of consensus on which method is the most suitable one for a given problem instance. This paper is the second part of a two-part survey on attribute weighting methods in MADM scenarios. The first part introduced a categorization in five classes while focusing on explicit weighting methods. The current paper addresses implicit and hybrid approaches. A total of 20 methods are analyzed in order to identify their strengths and limitations. Toward the end, we discuss possible alternatives to address the detected drawbacks, thus paving the road for further research directions. The implicit weighting with additional information category resulted in the most coherent approach to give effective solutions. Consequently, we encourage the development of future methods with additional preference information. [ABSTRACT FROM AUTHOR]

Naive Bayes classifier (NBC) is an effective classification technique in data mining and machine learning, which is based on the attribute conditional independence assumption. However, this assumption rarely holds true in real-world applications, so numerous researches have been made to alleviate the assumption by attribute weighting. To the best of our knowledge, almost all studies have calculated attribute weights according to correlation measure or classification accuracy. In this paper, we propose a novel causality-based attribute weighting method to establish the weighted NBC called IFG-WNBC, where causal information flow (IF) theory and genetic algorithm (GA) are adopted to search for optimal weights. The introduction of IF produces a bran-new weight measure criterion from the angle of causality other than correlation. The population initialization in GA is also improved with IF-based weights for efficient optimization. Multi-set of comparison experiments on UCI data sets demonstrate that IFG-WNBC achieves superiority over classic NBC and other common weighted NBC algorithms in classification accuracy and running time.

Consumers often have to make trade‐offs between desirable, "more is better," and undesirable, "less is better," attributes. What drives whether the desirable or the undesirable attributes will be weighed more heavily in decisions? We show that the extent to which consumers focus on desirable versus undesirable attributes depends on the overall attractiveness of their consideration sets. The less attractive the options under consideration are—the higher is the weight allocated to undesirable attributes, such as price. Three experiments set in the contexts of lottery ticket purchasing (study 1), hotel booking (study 2), elections (study 3), and a conjoint study of online course evaluations (study 4) (N = 2,149, p‐curve power estimate 90%), demonstrate that unattractive sets increase the relative weight of "undesirable" attributes (e.g., price of a product, workload of a course) and lead to increased preference for options superior on these attributes. [ABSTRACT FROM AUTHOR]

This research examines how the weighting of an attribute is jointly affected by attribute precision and decision stage. Building on prior work suggesting (a) that less (more) precise numerical values are easier to process (more accurate), (b) that decision‐makers' motivation to be efficient (accurate) is greater when creating a consideration set (making a final choice), and (c) that decision‐makers tend to overweight information that is compatible with their goals, we hypothesize that when creating a consideration set (making a choice) participants tend to assign greater weight to less (more) precise attributes. Five studies (two of them reported in the Appendix S1) offer triangulating evidence for these predictions. Overall, this work contributes to research on numerical cognition, efficiency versus accuracy trade‐offs, attribute weighting, and two‐stage decisions. [ABSTRACT FROM AUTHOR]

Due to Naive Bayes algorithm has good interpretability and performance, it is widely used to deal with classification problems. Naive Bayes assumes that the attributes are independent of each other. But the phenomenon of the correlations between attributes is always exists in fact. In most case the result of the classification will be strong influenced by these correlations. Thus, minimum the correlation among attributes has been deemed as a challenge for the Naive Bayes research community. In this work, we proposed an improved Naive Bayes method which uses the Laplacian matrix to reconstruct the dataset, since the laplacian matrix can describe the spatial relationship between data attributes well. Experiment results are shown that our method can greatly reduce the correlation between attributes. [ABSTRACT FROM AUTHOR]

Enhancing distance measures is key to improving the performances of many machine learning algorithms, such as instance-based learning algorithms. Although the inverted specific-class distance measure (ISCDM) is among the top performing distance measures addressing nominal attributes with the presence of missing values and non-class attribute noise in the training set, this still requires the attribute independence assumption. It is obvious that the attribute independence assumption required by the ISCDM is rarely true in reality, which harms its performance in applications with complex attribute dependencies. Thus, in this study we propose an improved ISCDM by utilizing attribute weighting to circumvent the attribute independence assumption. In our improved ISCDM, we simply define the weight of each attribute as its gain ratio. Thus, we denote our improved ISCDM as the gain ratio weighted ISCDM (GRWISCDM for short). We tested the GRWISCDM experimentally on 29 University of California at Irvine datasets, and found that it significantly outperforms the original ISCDM and some other state-of-the-art competitors in terms of the negative conditional log likelihood and root relative squared error. [ABSTRACT FROM AUTHOR]

Fuzzy rule interpolation (FRI) facilitates approximate reasoning in fuzzy rule-based systems only with sparse knowledge available, remedying the limitation of conventional compositional rule of inference working with a dense rule base. Most of the existing FRI work assumes equal significance of the conditional attributes in the rules while performing interpolation. Recently, interesting techniques have been reported for achieving weighted interpolative reasoning. However, they are either particularly tailored to perform classification problems only or employ attribute weights that are obtained using additional information (rather than just the given rules), without integrating them with the associated FRI procedure. This paper presents a weighted rule interpolation scheme for performing prediction tasks by the use of fuzzy sparse knowledge only. The weights of rule conditional attributes are learned from a given rule base to discriminate the relative significance of each individual attribute and are integrated throughout the internal mechanism of the FRI process. This scheme is demonstrated using the popular scale and move transformation-based FRI for resolving prediction problems, systematically evaluated on 12 benchmark prediction tasks. The performance is compared with the relevant state-of-the-art FRI techniques, showing the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

In this research, an attribute-weighted one-dependence Bayes estimation algorithm based on the asymmetric correlation coefficient is proposed. The asymmetric correlation coefficients Tau_y and Lambda_y, respectively, are used to calculate the correlation between parent attributes and category labels, then the result of calculation is regarded as weight to the parent attribute. The algorithm is applied to eight types of different datasets including binary classification and multiple classification from the UCI database. By comparing the time complexity and classification accuracy, experimental results show that the algorithm can significantly improve the classification performance with less prediction error. In addition, several baseline methods such as KNN, ANN, logistic regression and SVM are used for comparison with the proposed method. [ABSTRACT FROM AUTHOR]

Attribute weighting is a key aspect when modeling multi-attribute decision analysis problems. Despite the large number of proposals reported in the literature, reaching a consensus on the most convenient method for a certain scenario is difficult, if not impossible. As a first contribution of this paper, we propose a categorization of existing methodologies, which goes beyond the current taxonomy (subjective, objective, hybrid). As a second contribution, supported by the new categorization, we survey and critically discuss the explicit weighting methods (which are closely related to the subjective ones). The critical discussion allows evaluating how much a solution can deviate from the expected one if no foresight is taken. As a final contribution, we summarize the main drawbacks from a global perspective and propose some insights to correct them. Such a discussion attempts to improve the reliability of decision support systems involving human experts. [ABSTRACT FROM AUTHOR]

Outlier detection plays an important role in data mining because it can improve the performance of data analysis. Most outlier detection algorithms focus on numerical or categorical attributes; however, data typically have a mixture of numerical and categorical attributes. We addressed this problem by developing an attribute-weighted outlier detection algorithm, PMIOD, for high-dimensional and massive mixed data. The PMIOD algorithm adopts mutual information to measure attribute correlations and provides an attribute-weighting method for mixed data. Based on this, an attribute-weighted outlier detection method for mixed data was developed. Moreover, to improve the efficiency of mutual information computing for high-dimensional mixed data, the mutual information computing was parallelized on the Spark platform. We evaluated the proposed algorithm using ten UCI datasets and four synthetic datasets in comparison with widely used algorithms. Experiments were conducted to demonstrate the superiority of the results produced by the proposed algorithm. • An attribute weighting method for mixed data is presented based on mutual information. • Attribute weighted outlier detection for mixed data can improve the detection accuracy. • The Spark-based column transformation makes the outlier detection algorithm faster. • A large number of experiments verify the effectiveness of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Computational models and simulations often involve representations of decision-making processes. Numerous methods exist for representing decision-making at varied resolution levels based on the objectives of the simulation and the desired level of fidelity for validation. Decision making relies on the type of decision and the criteria that is appropriate for making the decision; therefore, decision makers can reach unique decisions that meet their own needs given the same information. Accounting for personalized weighting scales can help to reflect a more realistic state for a modeled system. To this end, this article reviews and summarizes eight multi-criteria decision analysis (MCDA) techniques that serve as options for reaching unique decisions based on personally and individually ranked criteria. These techniques are organized into a taxonomy of ratio assignment and approximate techniques, and the strengths and limitations of each are explored. We compare these techniques potential uses across the Agent-Based Modeling (ABM), System Dynamics (SD), and Discrete Event Simulation (DES) modeling paradigms to inform current researchers, students, and practitioners on the state-of-the-art and to enable new researchers to utilize methods for modeling multi-criteria decisions.

This work was proposed as the first objective to demonstrate that a consistency index proposed in 2015 as part of an attribute weighting model is not objective enough. The second objective is the proposal of a new optimization model to weight attributes that is based on an idea similar to that of the analyzed model where the limitations detected in it are not evident. To achieve the above, the significance and scope of the consistency and inconsistency indices proposed in the previous work from where the limitation was induced in the first one is corroborated with the analysis of a case study. The new model is based on extended fuzzy logic techniques and on new concepts introduced by the authors such as the weighted generalized inconsistency index of order q and the internal and external confidence coefficients. [ABSTRACT FROM AUTHOR]

Due to its simplicity, efficiency and efficacy, value difference metric (VDM) has continued to perform well against more sophisticated newcomers and thus has remained of great interest to the distance metric learning community. Of numerous approaches to improving VDM by weakening its attribute independence assumption, attribute weighting has received less attention (only two attribute weighting schemes) but demonstrated remarkable class probability estimation performance. Among two existing attribute weighting schemes, one is non-symmetric and the other is symmetric. In this paper, we propose two simple improvements for setting attribute weights for use with VDM. One is the non-symmetric Kullback–Leibler divergence weighted value difference metric (KLD-VDM) and the other is the symmetric gain ratio weighted value difference metric (GR-VDM). We performed extensive evaluations on a large number of datasets and found that KLD-VDM and GR-VDM significantly outperform two existing attribute weighting schemes in terms of the negative conditional log likelihood and root relative squared error, yet at the same time maintain the computational simplicity and robustness that characterize VDM. [ABSTRACT FROM AUTHOR]

Highlights • Almost all existing attribute weighting approaches to naive Bayes are class-independent. • We propose a new class-specific attribute weighting paradigm for naive Bayes. • The resulting model is called class-specific attribute weighted naive Bayes (CAWNB). • To learn CAWNB, we propose two gradient-based learning algorithms. • The experimental results validate the effectiveness of the proposed algorithms. Abstract Due to its easiness to construct and interpret, along with its good performance, naive Bayes (NB) is widely used to address classification problems in real-world applications. In order to alleviate its conditional independence assumption, a mass of attribute weighting approaches have been proposed. However, almost all these approaches assign each attribute a same (global) weight for all classes. In this paper, we call them the general attribute weighting and argue that for NB attribute weighting should be class-specific (class-dependent). Based on this premise, we propose a new paradigm for attribute weighting called the class-specific attribute weighting, which discriminatively assigns each attribute a specific weight for each class. We call the resulting model class-specific attribute weighted naive Bayes (CAWNB). CAWNB selects class-specific attribute weights to maximize the conditional log likelihood (CLL) objective function or minimize the mean squared error (MSE) objective function, and thus two different versions are created, which we denote as CAWNBCLL and CAWNBMSE, respectively. Extensive empirical studies show that CAWNBCLL and CAWNBMSE all obtain more satisfactory experimental results compared with NB and other existing state-of-the-art general attribute weighting approaches. We believe that for NB class-specific attribute weighting could be a more fine-grained attribute weighting approach than general attribute weighting. [ABSTRACT FROM AUTHOR]

Recently, many improved naive Bayes methods have been developed with enhanced discrimination capabilities. Among them, regularized naive Bayes (RNB) produces excellent performance by balancing the discrimination power and generalization capability. Data discretization is important in naive Bayes. By grouping similar values into one interval, the data distribution could be better estimated. However, existing methods including RNB often discretize the data into too few intervals, which may result in a significant information loss. To address this problem, we propose a semi-supervised adaptive discriminative discretization framework for naive Bayes, which could better estimate the data distribution by utilizing both labeled data and unlabeled data through pseudo-labeling techniques. The proposed method also significantly reduces the information loss during discretization by utilizing an adaptive discriminative discretization scheme, and hence greatly improves the discrimination power of classifiers. The proposed RNB+, i.e., regularized naive Bayes utilizing the proposed discretization framework, is systematically evaluated on a wide range of machine-learning datasets. It significantly and consistently outperforms state-of-the-art NB classifiers. • We identify the significant information loss in previous discretization methods. • We propose a Semi-supervised Adaptive Discriminative Discretization (SADD) method. • The proposed SADD is integrated with regularized naïve Bayes, namely RNB+. • The proposed SADD effectively enhances the discrimination power of NB classifiers. • Experimental results on 31 UCI datasets validate the effectiveness of proposed SADD. [ABSTRACT FROM AUTHOR]