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

Tools

Chernbumroong, S., Cang, S. ORCID: 0000-0002-7984-0728 and Yu, H., 2015. Maximum relevancy maximum complementary feature selection for multi-sensor activity recognition. Expert Systems with Applications, 42 (1), pp. 573-583. ISSN 0957-4174

Preview

Text
1357098 Cang2.pdf - Post-print
Download (269kB) | Preview

Official URL: https://doi.org/10.1016/j.eswa.2014.07.052

Abstract

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.

Item Type:

Journal article

Publication Title:

Expert Systems with Applications

Creators:

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

Publisher:

Elsevier

Date:

January 2015

Volume:

Number: