Understanding Mahalanobis Distance Criterion for Feature Selection

Distance criteria are widely applied in cluster analysis and classification techniques. One of the well known and most commonly used distance criteria is the Mahalanobis distance, introduced by P. C. Mahalanobis in 1936. The functions of this distance have been extended to different problems such a...

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Main Authors:	Maz Jamilah, Masnan, Nor Idayu, Mahat, Ali Yeon, Md Shakaff, Abu Hassan, Abdullah, Nur Zawatil, Ishqi Zakaria, Nurlisa, Yusuf, Norazian, Subari, Ammar, Zakaria, Abdul Hallis, Abdul Aziz
Format:	Conference or Workshop Item
Language:	English English
Published:	2015
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/10467/ http://umpir.ump.edu.my/id/eprint/10467/ http://umpir.ump.edu.my/id/eprint/10467/1/Understanding%20Mahalanobis%20Distance%20Criterion%20for%20Feature%20Selection.pdf http://umpir.ump.edu.my/id/eprint/10467/6/Understanding%20Mahalanobis%20Distance%20Criterion%20for%20Feature%20Selection.pdf

id	ump-10467
recordtype	eprints
spelling	ump-104672018-02-19T05:13:00Z http://umpir.ump.edu.my/id/eprint/10467/ Understanding Mahalanobis Distance Criterion for Feature Selection Maz Jamilah, Masnan Nor Idayu, Mahat Ali Yeon, Md Shakaff Abu Hassan, Abdullah Nur Zawatil, Ishqi Zakaria Nurlisa, Yusuf Norazian, Subari Ammar, Zakaria Abdul Hallis, Abdul Aziz TK Electrical engineering. Electronics Nuclear engineering Distance criteria are widely applied in cluster analysis and classification techniques. One of the well known and most commonly used distance criteria is the Mahalanobis distance, introduced by P. C. Mahalanobis in 1936. The functions of this distance have been extended to different problems such as detection of multivariate outliers, multivariate statistical testing, and class prediction problems. In the class prediction problems, researcher is usually burdened with problems of excessive features where useful and useless features are all drawn for classification task. Therefore, this paper tries to highlight the procedure of exploiting this criterion in selecting the best features for further classification process. Classification performance for the feature subsets of the ordered features based on the Mahalanobis distance criterion is included. 2015 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/10467/1/Understanding%20Mahalanobis%20Distance%20Criterion%20for%20Feature%20Selection.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/10467/6/Understanding%20Mahalanobis%20Distance%20Criterion%20for%20Feature%20Selection.pdf Maz Jamilah, Masnan and Nor Idayu, Mahat and Ali Yeon, Md Shakaff and Abu Hassan, Abdullah and Nur Zawatil, Ishqi Zakaria and Nurlisa, Yusuf and Norazian, Subari and Ammar, Zakaria and Abdul Hallis, Abdul Aziz (2015) Understanding Mahalanobis Distance Criterion for Feature Selection. In: AIP Conf. Proc., 1660, 050075 : International Conference on Mathematics, Engineering and Industrial Applications (ICoMEIA 2014), 28-30 May 2014 , Penang. pp. 1-6.. http://dx.doi.org/10.1063/1.4915708
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Maz Jamilah, Masnan Nor Idayu, Mahat Ali Yeon, Md Shakaff Abu Hassan, Abdullah Nur Zawatil, Ishqi Zakaria Nurlisa, Yusuf Norazian, Subari Ammar, Zakaria Abdul Hallis, Abdul Aziz Understanding Mahalanobis Distance Criterion for Feature Selection
description	Distance criteria are widely applied in cluster analysis and classification techniques. One of the well known and most commonly used distance criteria is the Mahalanobis distance, introduced by P. C. Mahalanobis in 1936. The functions of this distance have been extended to different problems such as detection of multivariate outliers, multivariate statistical testing, and class prediction problems. In the class prediction problems, researcher is usually burdened with problems of excessive features where useful and useless features are all drawn for classification task. Therefore, this paper tries to highlight the procedure of exploiting this criterion in selecting the best features for further classification process. Classification performance for the feature subsets of the ordered features based on the Mahalanobis distance criterion is included.
format	Conference or Workshop Item
author	Maz Jamilah, Masnan Nor Idayu, Mahat Ali Yeon, Md Shakaff Abu Hassan, Abdullah Nur Zawatil, Ishqi Zakaria Nurlisa, Yusuf Norazian, Subari Ammar, Zakaria Abdul Hallis, Abdul Aziz
author_facet	Maz Jamilah, Masnan Nor Idayu, Mahat Ali Yeon, Md Shakaff Abu Hassan, Abdullah Nur Zawatil, Ishqi Zakaria Nurlisa, Yusuf Norazian, Subari Ammar, Zakaria Abdul Hallis, Abdul Aziz
author_sort	Maz Jamilah, Masnan
title	Understanding Mahalanobis Distance Criterion for Feature Selection
title_short	Understanding Mahalanobis Distance Criterion for Feature Selection
title_full	Understanding Mahalanobis Distance Criterion for Feature Selection
title_fullStr	Understanding Mahalanobis Distance Criterion for Feature Selection
title_full_unstemmed	Understanding Mahalanobis Distance Criterion for Feature Selection
title_sort	understanding mahalanobis distance criterion for feature selection
publishDate	2015
url	http://umpir.ump.edu.my/id/eprint/10467/ http://umpir.ump.edu.my/id/eprint/10467/ http://umpir.ump.edu.my/id/eprint/10467/1/Understanding%20Mahalanobis%20Distance%20Criterion%20for%20Feature%20Selection.pdf http://umpir.ump.edu.my/id/eprint/10467/6/Understanding%20Mahalanobis%20Distance%20Criterion%20for%20Feature%20Selection.pdf
first_indexed	2023-09-18T22:10:06Z
last_indexed	2023-09-18T22:10:06Z
_version_	1777414970031472640

Understanding Mahalanobis Distance Criterion for Feature Selection

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