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Journal of Sensors
Volume 2018, Article ID 3419213, 12 pages
Research Article

Evolutionary Multilabel Feature Selection Using Promising Feature Subset Generation

School of Computer Science and Engineering, Chung-Ang University, 221, Heukseok-Dong, Dongjak-Gu, Seoul 06974, Republic of Korea

Correspondence should be addressed to Dae-Won Kim;

Received 8 June 2018; Accepted 7 August 2018; Published 18 September 2018

Academic Editor: Grigore Stamatescu

Copyright © 2018 Jaesung Lee et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Recent progress in the development of sensor devices improves information harvesting and allows complex but intelligent applications based on learning hidden relations between collected sensor data and objectives. In this scenario, multilabel feature selection can play an important role in achieving better learning accuracy when constrained with limited resources. However, existing multilabel feature selection methods are search-ineffective because generated feature subsets frequently include unimportant features. In addition, only a few feature subsets compared to the search space are considered, yielding feature subsets with low multilabel learning accuracy. In this study, we propose an effective multilabel feature selection method based on a novel feature subset generation procedure. Experimental results demonstrate that the proposed method can identify better feature subsets than conventional methods.