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Feature Selection and Overlapping Clustering-Based Multilabel Classification Model
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-01-04
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Multilabel classification (MLC) learning, which is widely applied in real-world applications, is a very important problem in machine learning.
Some studies show that a clustering-based MLC framework performs effectively compared to a nonclustering framework.
In this paper, we explore the clustering-based MLC problem.
Multilabel feature selection also plays an important role in classification learning because many redundant and irrelevant features can degrade performance and a good feature selection algorithm can reduce computational complexity and improve classification accuracy.
In this study, we consider feature dependence and feature interaction simultaneously, and we propose a multilabel feature selection algorithm as a preprocessing stage before MLC.
Typically, existing cluster-based MLC frameworks employ a hard cluster method.
In practice, the instances of multilabel datasets are distinguished in a single cluster by such frameworks; however, the overlapping nature of multilabel instances is such that, in real-life applications, instances may not belong to only a single class.
Therefore, we propose a MLC model that combines feature selection with an overlapping clustering algorithm.
Experimental results demonstrate that various clustering algorithms show different performance for MLC, and the proposed overlapping clustering-based MLC model may be more suitable.
American Psychological Association (APA)
Peng, Liwen& Liu, Yongguo. 2018. Feature Selection and Overlapping Clustering-Based Multilabel Classification Model. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1206478
Modern Language Association (MLA)
Peng, Liwen& Liu, Yongguo. Feature Selection and Overlapping Clustering-Based Multilabel Classification Model. Mathematical Problems in Engineering No. 2018 (2018), pp.1-12.
https://search.emarefa.net/detail/BIM-1206478
American Medical Association (AMA)
Peng, Liwen& Liu, Yongguo. Feature Selection and Overlapping Clustering-Based Multilabel Classification Model. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1206478
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1206478