Maximum relevancy maximum complementary feature selection for multi-sensor activity recognition

This source preferred by Hongnian Yu and Shuang Cang

Authors: Chernbumroong, S., Cang, S. and Yu, H.

http://eprints.bournemouth.ac.uk/22667/

Journal: Expert Systems with Applications

Volume: 42

Issue: 1

Pages: 573-583

DOI: 10.1016/j.eswa.2014.07.052

This source preferred by Hongnian Yu and Shuang Cang

Authors: Chernbumroong, S., Cang, S. and Yu, H.

http://eprints.bournemouth.ac.uk/22667/

Journal: Expert Systems with Applications

Volume: 42

Issue: 1

Pages: 573-583

This data was imported from DBLP:

Authors: Chernbumroong, S., Cang, S. and Yu, H.

http://eprints.bournemouth.ac.uk/22667/

Journal: Expert Syst. Appl.

Volume: 42

Pages: 573-583

DOI: 10.1016/j.eswa.2014.07.052

This data was imported from Scopus:

Authors: Chernbumroong, S., Cang, S. and Yu, H.

http://eprints.bournemouth.ac.uk/22667/

Journal: Expert Systems with Applications

Volume: 42

Issue: 1

Pages: 573-583

ISSN: 0957-4174

DOI: 10.1016/j.eswa.2014.07.052

© 2014 Elsevier Ltd. All rights reserved. In the multi-sensor activity recognition domain, the input space is often large and contains irrelevant and overlapped features. It is important to perform feature selection in order to select the smallest number of features which can describe the outputs. This paper proposes a new feature selection algorithms using the maximal relevance and maximal complementary (MRMC) based on neural networks. Unlike other feature selection algorithms that are based on relevance and redundancy measurements, the idea of how a feature complements to the already selected features is utilized. The proposed algorithm is evaluated on two well-defined problems and five real world data sets. The data sets cover different types of data i.e. real, integer and category and sizes i.e. small to large set of features. The experimental results show that the MRMC can select a smaller number of features while achieving good results. The proposed algorithm can be applied to any type of data, and demonstrate great potential for the data set with a large number of features.

This data was imported from Web of Science (Lite):

Authors: Chernbumroong, S., Cang, S. and Yu, H.

http://eprints.bournemouth.ac.uk/22667/

Journal: EXPERT SYSTEMS WITH APPLICATIONS

Volume: 42

Issue: 1

Pages: 573-583

eISSN: 1873-6793

ISSN: 0957-4174

DOI: 10.1016/j.eswa.2014.07.052

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