A systematic review of multi-label feature selection and a new method based on label construction

Spolaôr, Newton; Monard, Maria Carolina; Tsoumakas, Grigorios; Lee, Huei

doi:10.1016/j.neucom.2015.07.118

Cited by 77 publications

(29 citation statements)

References 40 publications

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“…In multilabel feature selection studies, one of the major trends is the application of a feature selection method for single-label problems by transforming multilabel datasets into single-label datasets [32,33]. Although this strategy facilitates the use of conventional methods, which has advantages in terms of ease of use [34], algorithm adaptation strategies that directly manage multilabel problems have also been considered [35].…”

Section: Related Workmentioning

confidence: 99%

Effective Evolutionary Multilabel Feature Selection under a Budget Constraint

2018

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Multilabel feature selection involves the selection of relevant features from multilabeled datasets, resulting in improved multilabel learning accuracy. Evolutionary search-based multilabel feature selection methods have proved useful for identifying a compact feature subset by successfully improving the accuracy of multilabel classification. However, conventional methods frequently violate budget constraints or result in inefficient searches due to ineffective exploration of important features. In this paper, we present an effective evolutionary search-based feature selection method for multilabel classification with a budget constraint. The proposed method employs a novel exploration operation to enhance the search capabilities of a traditional genetic search, resulting in improved multilabel classification. Empirical studies using 20 real-world datasets demonstrate that the proposed method outperforms conventional multilabel feature selection methods.

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Section: Related Workmentioning

confidence: 99%

Effective Evolutionary Multilabel Feature Selection under a Budget Constraint

2018

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“…A major trend in multi-label feature selection studies involves applying a feature selection method after transforming label sets into one or more labels [16,25,36]. Based on this approach, the feature selection process can be performed after the transformation process is completed.…”

Section: A Brief Review Of Multi-label Feature Selectionmentioning

confidence: 99%

“…Thus, the label transformation process will require a prohibitive execution time if the multi-label dataset is composed of a large number of patterns and labels. Although the computational cost of the transformation process can be remedied by applying a simple procedure [16,39], an inefficient feature selection process can occur if the scoring process incurs excessive computational costs when evaluating the importance of features [26,34]. For example, PPT + RF identifies appropriate weight values for features based on a label that is transformed by the Pruned Problem Transformation (PPT) [39] and the conventional ReliefF (RF) scheme for single-label feature selection [40].…”

Section: A Brief Review Of Multi-label Feature Selectionmentioning

confidence: 99%

“…Although the characteristics of multi-label feature selection methods can vary according to how the importance of features is modeled, conventional methods create a feature subset by scoring the importance of features either for all labels [16,25,33] or all possible combinations drawn from the label set [17,18,27]. Thus, these methods inherently suffer from prohibitive computational costs when the dataset is composed of a large number of labels.…”

Section: A Brief Review Of Multi-label Feature Selectionmentioning

confidence: 99%

“…, b |L| ) = {0, 1} |L| representing the joint state of the label set where each element is one if the label is relevant and zero otherwise [13]. Under the multi-label learning umbrella, the goal of multi-label feature selection is to determine a subset of important features for multiple labels [14][15][16]. This problem can be solved by selecting a subset S composed of n features from F that jointly have the largest dependency on the labels L. Thus, multi-label feature selection can be achieved through a scoring process that assesses the importance of |F| features and selects the top-ranked n |F| features for inclusion in the feature subset S [17].…”

Section: Introductionmentioning

confidence: 99%

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Efficient Multi-Label Feature Selection Using Entropy-Based Label Selection

Lee

Kim

2016

Entropy

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Multi-label feature selection is designed to select a subset of features according to their importance to multiple labels. This task can be achieved by ranking the dependencies of features and selecting the features with the highest rankings. In a multi-label feature selection problem, the algorithm may be faced with a dataset containing a large number of labels. Because the computational cost of multi-label feature selection increases according to the number of labels, the algorithm may suffer from a degradation in performance when processing very large datasets. In this study, we propose an efficient multi-label feature selection method based on an information-theoretic label selection strategy. By identifying a subset of labels that significantly influence the importance of features, the proposed method efficiently outputs a feature subset. Experimental results demonstrate that the proposed method can identify a feature subset much faster than conventional multi-label feature selection methods for large multi-label datasets.

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Multilabel feature selection: A comprehensive review and guiding experiments

Kashef

Nezamabadi‐pour

Nikpour

2018

WIREs Data Min & Knowl

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Feature selection has been an important issue in machine learning and data mining, and is unavoidable when confronting with high‐dimensional data. With the advent of multilabel (ML) datasets and their vast applications, feature selection methods have been developed for dimensionality reduction and improvement of the classification performance. In this work, we provide a comprehensive review of the existing multilabel feature selection (ML‐FS) methods, and categorize these methods based on different perspectives. As feature selection and data classification are closely related to each other, we provide a review on ML learning algorithms as well. Also, to facilitate research in this field, a section is provided for setup and benchmarking that presents evaluation measures, standard datasets, and existing software for ML data. At the end of this survey, we discuss some challenges and open problems in this field that can be pursued by researchers in future. WIREs Data Mining Knowl Discov 2018, 8:e1240. doi: 10.1002/widm.1240 This article is categorized under: Technologies > Data Preprocessing

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A systematic review of multi-label feature selection and a new method based on label construction

Cited by 77 publications

References 40 publications

Effective Evolutionary Multilabel Feature Selection under a Budget Constraint

Effective Evolutionary Multilabel Feature Selection under a Budget Constraint

Efficient Multi-Label Feature Selection Using Entropy-Based Label Selection

Multilabel feature selection: A comprehensive review and guiding experiments

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