A label-specific multi-label feature selection algorithm based on the Pareto dominance concept

Kashef, Shima; Nezamabadi‐pour, Hossein

doi:10.1016/j.patcog.2018.12.020

Cited by 70 publications

(28 citation statements)

References 27 publications

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“…Li et al [ 10 ] proposed a granular MLFS method that attempts to select a more compact feature subset using information granules of the labels instead of the entire label set. Kashef and Nezamabadi-pour [ 11 ] proposed a Pareto dominance-based multilabel feature filter for online feature selection, which concerns the number of features being added sequentially. Gonzalez-Lopez et al [ 12 , 13 ] proposed distributed models that measure the quality of each feature based on mutual information on Apache Spark.…”

Section: Related Workmentioning

confidence: 99%

Multi-Population Genetic Algorithm for Multilabel Feature Selection Based on Label Complementary Communication

Park

Kim

et al. 2020

Entropy

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Multilabel feature selection is an effective preprocessing step for improving multilabel classification accuracy, because it highlights discriminative features for multiple labels. Recently, multi-population genetic algorithms have gained significant attention with regard to feature selection studies. This is owing to their enhanced search capability when compared to that of traditional genetic algorithms that are based on communication among multiple populations. However, conventional methods employ a simple communication process without adapting it to the multilabel feature selection problem, which results in poor-quality final solutions. In this paper, we propose a new multi-population genetic algorithm, based on a novel communication process, which is specialized for the multilabel feature selection problem. Our experimental results on 17 multilabel datasets demonstrate that the proposed method is superior to other multi-population-based feature selection methods.

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Section: Related Workmentioning

confidence: 99%

Multi-Population Genetic Algorithm for Multilabel Feature Selection Based on Label Complementary Communication

Park

Kim

et al. 2020

Entropy

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“…In recent years, many algorithm adaptation-based multi-label feature selection methods that directly select features from the multi-label data set have been proposed. S Kashef and H Nezamabadi-pour [ 15 ] propose a multi-label feature selection algorithm based on the Pareto dominance concept that intends to select the label-specific features in multi-objective optimization problem. Sun et al.…”

Section: Related Workmentioning

confidence: 99%

“…The high-dimensional multi-label data set often contains a large number of irrelevant and redundant features that bring many disadvantages to the multi-label learning such as the computational burden and over-fitting [ 10 , 11 , 12 ]. To address this problem, many multi-label feature selection techniques have been proposed to select the informative feature subset from the original feature set and to discard irrelevant and redundant features [ 13 , 14 , 15 ]. Feature selection techniques not only reduce the computing costs but also improve the classification performance effectively [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Label Feature Selection Based on High-Order Label Correlation Assumption

Zhang

Gao

et al. 2020

Entropy

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Multi-label data often involve features with high dimensionality and complicated label correlations, resulting in a great challenge for multi-label learning. Feature selection plays an important role in multi-label learning to address multi-label data. Exploring label correlations is crucial for multi-label feature selection. Previous information-theoretical-based methods employ the strategy of cumulative summation approximation to evaluate candidate features, which merely considers low-order label correlations. In fact, there exist high-order label correlations in label set, labels naturally cluster into several groups, similar labels intend to cluster into the same group, different labels belong to different groups. However, the strategy of cumulative summation approximation tends to select the features related to the groups containing more labels while ignoring the classification information of groups containing less labels. Therefore, many features related to similar labels are selected, which leads to poor classification performance. To this end, Max-Correlation term considering high-order label correlations is proposed. Additionally, we combine the Max-Correlation term with feature redundancy term to ensure that selected features are relevant to different label groups. Finally, a new method named Multi-label Feature Selection considering Max-Correlation (MCMFS) is proposed. Experimental results demonstrate the classification superiority of MCMFS in comparison to eight state-of-the-art multi-label feature selection methods.

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“…At present, multilabel classification has drawn widespread attention, and the multilabel data with a set of labels contain a vast number of noisy, redundant or irrelevant features, and they decrease classification accuracy [1]. As an important preprocessing part of multilabel classification, feature selection aims to remove redundant or irrelevant features, eliminate the curse of dimensionality, classify data and obtain useful information to improve classification performance [2].…”

Section: Introductionmentioning

confidence: 99%

Multilabel Feature Selection Using Mutual Information and ML-ReliefF for Multilabel Classification

Shi

Sun

et al. 2020

IEEE Access

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Recently, multilabel classification algorithms play an increasingly significant role in data mining and machine learning. However, some existing mutual information-based algorithms ignore the influence of the proportions of labels on the correlation degree between features and label sets. Besides, the correlation degree of label sets cannot be accurately measured in most traditional ReliefF algorithms, and the repeated calculation arises from the division of heterogeneous neighbors. To overcome these shortcomings, this paper proposes a multilabel feature selection method using mutual information and improved multilabel ReliefF (ML-ReliefF). First, the proportion of each label is calculated in label space and combined with the mutual information of features and labels to construct a novel correlation degree between features and label sets to preprocess multilabel datasets, which is used to reduce runtime of ML-ReliefF. Second, the mutual information of label sets is introduced into improving accuracy of the correlation degree among label sets. Furthermore, two types of correlation degree for label sets based on ML-ReliefF are developed to divide similar and heterogeneous samples more clearly. Third, a divided method of heterogeneous neighbors is presented to effectively avoid the repeated calculation in ML-ReliefF, and a novel method of feature weighting based on ML-ReliefF is constructed to evaluate the importance of features. Finally, a multilabel feature selection algorithm based on mutual information and ML-ReliefF for multilabel classification is designed to improve the performance of multilabel classification. Experiments under fourteen multilabel datasets show the effectiveness of our algorithm and improve the classification performance for multilabel datasets.

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A label-specific multi-label feature selection algorithm based on the Pareto dominance concept

Cited by 70 publications

References 27 publications

Multi-Population Genetic Algorithm for Multilabel Feature Selection Based on Label Complementary Communication

Multi-Population Genetic Algorithm for Multilabel Feature Selection Based on Label Complementary Communication

Multi-Label Feature Selection Based on High-Order Label Correlation Assumption

Multilabel Feature Selection Using Mutual Information and ML-ReliefF for Multilabel Classification

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