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 as d...
| Main Authors: | , , , , , , , , |
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| Format: | Conference or Workshop Item |
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2015
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| author | Masnan, Maz Jamilah Mahat, Nor Idayu Md Shakaff, Ali Yeon Abdullah, Abu Hassan Zakaria, Nur Zawatil Ishqi Yusuf, Nurlisa Subari, Norazian Zakaria, Ammar Abdul Aziz, Abdul Hallis |
| author_facet | Masnan, Maz Jamilah Mahat, Nor Idayu Md Shakaff, Ali Yeon Abdullah, Abu Hassan Zakaria, Nur Zawatil Ishqi Yusuf, Nurlisa Subari, Norazian Zakaria, Ammar Abdul Aziz, Abdul Hallis |
| author_sort | Masnan, Maz Jamilah |
| collection | UUM |
| 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. |
| first_indexed | 2024-07-04T06:08:32Z |
| format | Conference or Workshop Item |
| id | uum-18750 |
| institution | Universiti Utara Malaysia |
| last_indexed | 2024-07-04T06:08:32Z |
| publishDate | 2015 |
| record_format | eprints |
| spelling | uum-187502016-10-04T06:47:33Z https://repo.uum.edu.my/id/eprint/18750/ Understanding Mahalanobis distance criterion for feature selection Masnan, Maz Jamilah Mahat, Nor Idayu Md Shakaff, Ali Yeon Abdullah, Abu Hassan Zakaria, Nur Zawatil Ishqi Yusuf, Nurlisa Subari, Norazian Zakaria, Ammar Abdul Aziz, Abdul Hallis QA Mathematics 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 Masnan, Maz Jamilah and Mahat, Nor Idayu and Md Shakaff, Ali Yeon and Abdullah, Abu Hassan and Zakaria, Nur Zawatil Ishqi and Yusuf, Nurlisa and Subari, Norazian and Zakaria, Ammar and Abdul Aziz, Abdul Hallis (2015) Understanding Mahalanobis distance criterion for feature selection. In: International Conference on Mathematics, Engineering and Industrial Applications 2014, 28–30 May 2014, Penang, Malaysia. http://doi.org/10.1063/1.4915708 doi:10.1063/1.4915708 doi:10.1063/1.4915708 |
| spellingShingle | QA Mathematics Masnan, Maz Jamilah Mahat, Nor Idayu Md Shakaff, Ali Yeon Abdullah, Abu Hassan Zakaria, Nur Zawatil Ishqi Yusuf, Nurlisa Subari, Norazian Zakaria, Ammar Abdul Aziz, Abdul Hallis Understanding Mahalanobis distance criterion for feature selection |
| title | 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_short | Understanding Mahalanobis distance criterion for feature selection |
| title_sort | understanding mahalanobis distance criterion for feature selection |
| topic | QA Mathematics |
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