Feature selection via dimensionality reduction for object class recognition
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...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2011
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| Online Access: | http://psasir.upm.edu.my/id/eprint/48177/ http://psasir.upm.edu.my/id/eprint/48177/1/Feature%20selection%20via%20dimensionality%20reduction%20for%20object%20class%20recognition.pdf |
| _version_ | 1848851010810806272 |
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| author | Manshor, Noridayu Abdul Halin, Alfian Rajeswari, Mandava Ramachandram, Dhanesh |
| author_facet | Manshor, Noridayu Abdul Halin, Alfian Rajeswari, Mandava Ramachandram, Dhanesh |
| author_sort | Manshor, Noridayu |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T10:15:23Z |
| format | Conference or Workshop Item |
| id | upm-48177 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T10:15:23Z |
| publishDate | 2011 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-481772016-08-03T08:09:03Z http://psasir.upm.edu.my/id/eprint/48177/ Feature selection via dimensionality reduction for object class recognition Manshor, Noridayu Abdul Halin, Alfian Rajeswari, Mandava Ramachandram, Dhanesh 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. IEEE 2011 Conference or Workshop Item PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/48177/1/Feature%20selection%20via%20dimensionality%20reduction%20for%20object%20class%20recognition.pdf 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). 10.1109/ICICI-BME.2011.6108645 |
| spellingShingle | Manshor, Noridayu Abdul Halin, Alfian Rajeswari, Mandava Ramachandram, Dhanesh Feature selection via dimensionality reduction for object class recognition |
| title | Feature selection via dimensionality reduction for object class recognition |
| title_full | Feature selection via dimensionality reduction for object class recognition |
| title_fullStr | Feature selection via dimensionality reduction for object class recognition |
| title_full_unstemmed | Feature selection via dimensionality reduction for object class recognition |
| title_short | Feature selection via dimensionality reduction for object class recognition |
| title_sort | feature selection via dimensionality reduction for object class recognition |
| url | http://psasir.upm.edu.my/id/eprint/48177/ http://psasir.upm.edu.my/id/eprint/48177/ http://psasir.upm.edu.my/id/eprint/48177/1/Feature%20selection%20via%20dimensionality%20reduction%20for%20object%20class%20recognition.pdf |