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

Saisakul Chernbumroong, Shuang Cang, Hongnian Yu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)573-583
Number of pages11
JournalExpert Systems with Applications
Volume42
Issue number1
Early online date23 Aug 2014
DOIs
Publication statusPublished - 1 Jan 2015

Fingerprint Dive into the research topics of 'Maximum relevancy maximum complementary feature selection for multi-sensor activity recognition'. Together they form a unique fingerprint.

Cite this