Tools
Tools
Chernbumroong, S, Cang, S 
ORCID: https://orcid.org/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 |
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: | 42 |
| Number: | 1 |
| ISSN: | 0957-4174 |
| Identifiers: | Number Type 10.1016/j.eswa.2014.07.052 DOI S0957417414004680 Publisher Item Identifier 1357098 Other |
| Divisions: | Schools > School of Science and Technology |
| Record created by: | Linda Sullivan |
| Date Added: | 27 Aug 2020 09:50 |
| Last Modified: | 31 May 2021 15:17 |
| URI: | https://irep.ntu.ac.uk/id/eprint/40539 |
Actions (login required)
 |
Edit View |
Statistics
Views
Views per month over past year
Downloads
Downloads per month over past year
