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Open AccessArticle

Streaming Feature Selection for Multi-Label Data with Dynamic Sliding Windows and Feature Repulsion Loss

by Yu Li 1,2 and Yusheng Cheng 1,3,*
1
School of Computer and Information, Anqing Normal University, Anqing 246003, China
2
Lab of Multimedia and Recommendation Systems, Hefei University of Technology, Hefei 230009, China
3
The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing 246003, China
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(12), 1151; https://doi.org/10.3390/e21121151
Received: 28 September 2019 / Revised: 9 November 2019 / Accepted: 23 November 2019 / Published: 25 November 2019
(This article belongs to the Section Information Theory, Probability and Statistics)
In recent years, there has been a growing interest in the problem of multi-label streaming feature selection with no prior knowledge of the feature space. However, the algorithms proposed to handle this problem seldom consider the group structure of streaming features. Another shortcoming arises from the fact that few studies have addressed atomic feature models, and particularly, few have measured the attraction and repulsion between features. To remedy these shortcomings, we develop the streaming feature selection algorithm with dynamic sliding windows and feature repulsion loss (SF-DSW-FRL). This algorithm is essentially carried out in three consecutive steps. Firstly, within dynamic sliding windows, candidate streaming features that are strongly related to the labels in different feature groups are selected and stored in a fixed sliding window. Then, the interaction between features is measured by a loss function inspired by the mutual repulsion and attraction between atoms in physics. Specifically, one feature attraction term and two feature repulsion terms are constructed and combined to create the feature repulsion loss function. Finally, for the fixed sliding window, the best feature subset is selected according to this loss function. The effectiveness of the proposed algorithm is demonstrated through experiments on several multi-label datasets, statistical hypothesis testing, and stability analysis.
Keywords: multi-label learning; streaming feature selection; sliding window; feature repulsion loss multi-label learning; streaming feature selection; sliding window; feature repulsion loss
MDPI and ACS Style

Li, Y.; Cheng, Y. Streaming Feature Selection for Multi-Label Data with Dynamic Sliding Windows and Feature Repulsion Loss. Entropy 2019, 21, 1151.

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