MFS-MCDM: Multi-label feature selection using multi-criteria decision making

In this paper, for the first time, a feature selection procedure is modeled as a multi-criteria decision making (MCDM) process. This method is applied to a multi-label data and we have used the TOPSIS (Technique of Order Preference by Similarity to Ideal Solution) method as a famous MCDM algorithm t...

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Bibliographic Details
Published in:Knowledge-based systems 2020-10, Vol.206, p.106365, Article 106365
Main Authors: Hashemi, Amin, Dowlatshahi, Mohammad Bagher, Nezamabadi-pour, Hossein
Format: Article
Language:English
Subjects:
Algorithms
Data integration
Decision making
Entropy
Feature selection
Information fusion
Mathematical analysis
Matrix algebra
Matrix methods
Multi-criteria decision making
Multi-label feature selection
Multiple criteria decision making
Multiple criterion
Ridge regression
TOPSIS
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Summary:In this paper, for the first time, a feature selection procedure is modeled as a multi-criteria decision making (MCDM) process. This method is applied to a multi-label data and we have used the TOPSIS (Technique of Order Preference by Similarity to Ideal Solution) method as a famous MCDM algorithm to evaluate the features based on their relationship with multiple labels as different criteria. Our proposed method, Multi-label Feature Selection using Multi-Criteria Decision Making (MFS-MCDM) which treated the multi-label feature selection as an information fusion process, first obtains a decision matrix using the ridge regression algorithm and then calculates the weight of each column of this matrix based on the entropy of each label. Then, the TOPSIS approach is used to assign a score to each feature based on the weighted decision matrix. Finally, a rank vector for the features is generated as output which the user can select a desired number of features. The superiority of the proposed method is shown in experimental results comparing to other similar methods in terms of all evaluation metrics. •We have designed a method for feature selection on the multi-label data.•We model the feature selection process to a multi-criteria decision-making.•The TOPSIS method is used to rank the features.•A subspace learning using Ridge Regression is used to capture the correlation.•The proposed TOPSIS-based multi-label method outperforms competitive methods.
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2020.106365