Feature selection via dimensionality reduction for object class recognition

Citation

Manshor, Noridayu and Abdul Halin, Alfian and Rajeswari, Mandava and Ramachandram, Dhanesh (2011) Feature selection via dimensionality reduction for object class recognition. In: 2011 2nd International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME 2011), 8-9 Nov. 2011, Bandung, Indonesia. (pp. 223-227).

Abstract

This paper investigates the effects of feature selection via dimensionality reduction techniques for the task of object class recognition. Two filter-based algorithms are considered namely Correlation-based Feature Selection (CFS) and Principal Components Analysis (PCA). A Support Vector Machine is used to compare these two techniques against classical feature concatenation, based on the Graz02 dataset. Experimental results show that the feature selection algorithms are able to retain the most relevant and discriminant features, while maintaining recognition accuracy and improving model building time.

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Additional Metadata

Item Type:	Conference or Workshop Item (Paper)
Divisions:	Faculty of Computer Science and Information Technology
DOI Number:	https://doi.org/10.1109/ICICI-BME.2011.6108645
Publisher:	IEEE
Keywords:	Feature fusion; Feature selection; Filter model; Object class recognition; Support vector machine
Depositing User:	Nabilah Mustapa
Date Deposited:	03 Aug 2016 08:09
Last Modified:	03 Aug 2016 08:09
Altmetrics:	http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/ICICI-BME.2011.6108645
URI:	http://psasir.upm.edu.my/id/eprint/48177
Statistic Details:	View Download Statistic

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