Evolutionary Multilabel Feature Selection Using Promising Feature Subset Generation
Joint Authors
Lee, Jaesung
Seo, Wangduk
Kim, Dae-Won
Han, Ho
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-09-18
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Lee, Jaesung& Seo, Wangduk& Han, Ho& Kim, Dae-Won. 2018. Evolutionary Multilabel Feature Selection Using Promising Feature Subset Generation. Journal of Sensors،Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1201084
Modern Language Association (MLA)
Lee, Jaesung…[et al.]. Evolutionary Multilabel Feature Selection Using Promising Feature Subset Generation. Journal of Sensors No. 2018 (2018), pp.1-12.
https://search.emarefa.net/detail/BIM-1201084
American Medical Association (AMA)
Lee, Jaesung& Seo, Wangduk& Han, Ho& Kim, Dae-Won. Evolutionary Multilabel Feature Selection Using Promising Feature Subset Generation. Journal of Sensors. 2018. Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1201084
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1201084