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Article:Feature Usability Index and Optimal Feature Subset Selection
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 12 - Number 2 |
| Year of Publication: 2010 |
| Authors: Debdoot Sheet, Jyotirmoy Chatterjee, Hrushikesh Garud |
10.5120/1650-2219
|
Debdoot Sheet, Jyotirmoy Chatterjee, Hrushikesh Garud . Article:Feature Usability Index and Optimal Feature Subset Selection. International Journal of Computer Applications. 12, 2 ( December 2010), 29-36. DOI=10.5120/1650-2219
Abstract
Feature usability index is introduced here as a measure for evaluating classification efficacy of features. It is defined using measures of homogeneity, class specificity, and error in decision making. Homogeneity measures the extent of outlying observations, class specificity assesses the separation between distributions of different labeled classes, and error in decision making is computed using overlap in posteriori decision boundary. This is followed by feature ranking and optimal feature subset selection through ordering of features based on feature usability index and involves a complexity of O(DlogD) for D features. The results validating classifier independent feature ranking and optimal feature subset selection are also presented aong with a comparative analysis using χ2 statistics for feature selection.
References
- F. J. Anscombe and Irwin Guttman. Rejection of outliers. Technometrics, 2(2):123–147, May 1960.
- V. Barnett. The ordering of multivariate data. Journal of Royal Statistical Society, 139(3):318–355, 1976.
- Irad Ben-Gal. Outlier detection. In O. Maimon and L. Rockach, editors, Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers. Kluwer Acad. Pub., 2005.
- P. M. Churchland. Chimerical colors: Some novel predictions from cognitive neuroscience. In A. Brook and K. Akins, editors, Cognition and the Brain, pages 309–335. Cambridge University Press, 2005.
- T. M. Cover. The best two independent measurements are not the two best. Systems, Man and Cybernetics, IEEE Transactions on, SMC-4(1):116–117, Jan 1974.
- Richard O. Duda, Peter E. Hart, and David G. Stork. Pattern Classification. Wiley, 2001.
- Ali S. Hadi. Identifying multiple outliers in multivariate data. Journal of Royal Statistical Society, 54(3):761–771, 1992.
- Anil K. Jain and B. Chandrasekaran. Dimensionality and sample size considerations in pattern recognition practice. In P. R. Krishnaiah and L. N. Kanal, editors, Handbook of Statistics, pages 835–855. North Holland, Amsterdam, 1982.
- H. M. Kalayeh and D. A. Landgrebe. Predicting the required number of training samples. Pattern Analysis and Machine Intelligence, IEEE Transactions on, PAMI-5(6):664–667, Nov. 1983.
- H. Liu, E.R. Dougherty, J.G. Dy, K. Torkkola, E. Tuv, H. Peng, C. Ding, F. Long, M. Berens, L. Parsons, Z. Zhao, L. Yu, and G. Forman. Evolving feature selection. Intelligent Systems, IEEE, 20(6):64–76, Nov.-Dec. 2005.
- Huan Liu and Hiroshi Motoda. Computational Methods for Feature Selection. CRC Press, 2008.
- Huan Liu and Rudy Setiono. Incremental feature selection. Applied Intelligence, 9:217–230, 1998.
- Huan Liu and Lei Yu. Toward integrating feature selection algorithms for classification and clustering. Knowledge and Data Engineering, IEEE Transactions on, 17(4):491–502, April 2005.
- Olvi L. Mangasarian, W. Nick Street, and William H. Wolberg. Breast Cancer Diagnosis and Prognosis Via Linear Programming. Operations Research, 43(4):570–577, 1995.
- P. Mitra, C.A. Murthy, and S.K. Pal. Unsupervised feature selection using feature similarity. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(3):301–312, Mar 2002.
- Linda Mthembu and Tshilidzi Marwala. A note on the separability index. Online, 2008.
- Nobuyuki Otsu. A threshold selection method from gray-level histograms. Systems, Man and Cybernetics, IEEE Transactions on, 9(1):62–66, Jan. 1979.
- E. S. Pearson and C. Chandra Sekar. The efficiency of statistical tools and a criterion for the rejection of outlying observations. Biometrika, 28(3/4):308–320, Dec. 1936.
- Helene Schulerud and Fritz Albergtsen. Many are called, but few are chosen. feature selection and error estimation in high dimensional spaces. Computer Methods and Programs in Biomedicine, 73:91–99, 2004.
- J W Smith, J E Everhart, W C Dickson, W C Knowler, and R S Johannes. Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. In Annual Symposium on Computer Applications in Medical Care, Proceedings of, pages 261–265, Nov 1988.
- W. N. Street, W. H. Wolberg, and O. L. Mangasarian. Nuclear feature extraction for breast tumor diagnosis. In R. S. Acharya & D. B. Goldgof, editor, SPIE Conference Series, Proceedings of, volume 1905, pages 861–870, Jul 1993.
- Andrew Webb. Statistical Pattern Recognition. Wiley, 2002.
- S. S. Wilks. Multivariate statistical outliers. Sankhya, 25(4):407–426, 1963.
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