Dimensionality reduction with unsupervised feature selection and applying non-Euclidean norms for classification accuracy

International Journal of Data Warehousing and Mining

Volumn 6, Issue 2, 2010, Pages 22-40

  Saxena, Amit ^a   Wang, John ^b  

^a GURU GHASIDAS UNIVERSITY   (India)

^b MONTCLAIR STATE UNIVERSITY   (United States)

Author keywords

Classification; Data mining; Feature analysis; Genetic algorithms; Minkowski metric

Indexed keywords

CLASSIFICATION ACCURACY; DATA SETS; DIMENSIONALITY REDUCTION; EUCLIDEAN NORM; EXTENSIVE SIMULATIONS; FEATURE ANALYSIS; FEATURE SETS; FEATURE SUBSET SELECTION; FITNESS FUNCTIONS; K-NEAREST NEIGHBORS; MINKOWSKI; NON-EUCLIDEAN NORM; SECOND PHASE; TWO-PHASE SCHEME; UNSUPERVISED APPROACHES; UNSUPERVISED FEATURE SELECTION;

DATA HANDLING; DATA MINING; FEATURE EXTRACTION; FUNCTION EVALUATION; FUZZY CONTROL; GENETIC ALGORITHMS; ROUGH SET THEORY; STATISTICAL TESTS;

CLASSIFICATION (OF INFORMATION);

EID: 77954138930     PISSN: 15483924     EISSN: 15483932     Source Type: Journal    
DOI: 10.4018/jdwm.2010040102     Document Type: Article

References (31)

1. Aggarwal, C. C., & Hinneburg, D. A. Keim. (2001). On the surprising behaviour of distance metrics in high dimensional space, in Proceedings of the Eight International Conference on Data base Theory (pp. 420-434).. London, UK.
2. Pal, S. K., De, R. K., & Basak, J., (2000, March). Unsupervised feature selection using a neuro-fuzzy approach. IEEE Transactions on Neural Networks, 11 (2), 366-375. doi:10.1109/72.839007
3. Beyer, K., Goldstein, J., Ramakrishnan, R., & Shaft, U. (1999). When is nearest neighbor meaningful? In Proceedings of the Seventh International Conference on Database theory (pp. 217-235). Jerusalem, Israel.
4. Chow, T. W. S., & Huang, D. (2005, January). Estimating optimal feature subsets using efficient estimation of high-dimensional mutual information. IEEE Transactions on Neural Networks, 16 (1), 213-224. doi:10.1109/TNN.2004.841414 (Pubitemid 40241922)
5. Dash, M., Liu, H., & Yao, J. (1997). Dimensionality reduction for unsupervised data. In Proceedings of 19th IEEE International Conference on Tools with (AI, ICTAI '97).
6. Doherty, K. A. J., Adams, R. G., & Davey, N. (2004). Non-Euclidean norms and data normalization. In Proceedings of the 12h Euro. Symposium on Artificial Neural Networks (pp. 181-186). Brugges, Belgium.
7. Doherty, K. A. J., Adams, R. G., & Davey, N. (2007). Unsupervised learning with normalized data and non-Euclidean norms. Applied Soft Computing, 7, 203-210. doi:10.1016/j.asoc.2005.05.005 (Pubitemid 44739428)
8. Goldberg, D. (1989). Genetic algorithms in search, optimization and machine learning. Reading, MA: Addison-Wesley.
9. Han, J., & Kamber, M. (2006). Data mining: Concepts and techniques, (2nd ed.) San Francisco, CA: Morgan Kaufmann.
10. Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20 (8), 832-844. doi:10.1109/34.709601
11. Kohavi, R., & John, G. H. (1997). Wrappers for feature subset selection. Artificial Intelligence, 97, 273-324. doi:10.1016/S0004-3702 (97) 00043-X
12. Lim, L. (2000). A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms.. Machine Learning, 40, 203-229. doi:10.1023/A:1007608224229
13. Liu, H., & Yu, L. (2005, April). Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering, 17 (4), 491-502. doi:10.1109/TKDE.2005.66 (Pubitemid 40495592)
14. Mitra, P., Murthy, C. A., & Pal, S. K. (2002). Unsupervised feature selection using feature similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24 (3), 301-312. doi:10.1109/34.990133 (Pubitemid 35289348)
15. Muni, D. P., Pal, N. R., & Das, J. (2006, February). Genetic programming for simultaneous feature selection and classifier design. IEEE Transactions on Systems, Man, and Cybernetics-PART B, 36 (1), 106-117.
16. Pal, N. R. (1999). Soft computing for feature analysis. Fuzzy Sets and Systems, 103 (2), 201-221. doi:10.1016/S0165-0114 (98) 00222-X
17. Pal, N. R., Eluri, V. K., & Mandal, G. K. (2002, June). Fuzzy logic approaches to structure preserving dimensionality reduction. IEEE Transactions on Fuzzy Systems, 10 (3), 277-286. doi:10.1109/TFUZZ.2002.1006431 (Pubitemid 34807717)
18. Pena, J. M., Lozano, J. A., Larranaga, P., & Iwza, I. (2001). Dimensionality reduction in unsupervised learning of conditional Gaussian networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23 (6), 590-603. doi:10.1109/34.927460 (Pubitemid 32585272)
19. Quinlan, J. R. (1993). C4.5: Programs for machine learning. San Mateo, CA: Morgan Kaufmann.
20. Raymer, M. L., Punch, W. F., Goodman, E. D., Kuhn, L. A., & Jain, A. K. (2000, July). Dimensionality reduction using genetic algorithms. IEEE Transactions on Evolutionary Computation, 4 (2), 164-171. doi:10.1109/4235. 850656
21. Romero, O., & Abelló, A. (2009). A survey of multidimensional modeling methodologies. International Journal of Data Warehousing and Mining, 5 (2), 1-23.
22. Ruiz, R., Riquelme, J., & Aguilar-Ruiz, J. (2006). Incremental wrapper-based gene selection from microarray data for cancer classification. Pattern Recognition, 39, 2383-2392. doi:10.1016/j. patcog.2005.11.001 (Pubitemid 44344733)
23. Sammon, J. W. Jr. (1969). A nonlinear mapping for data structure analysis. IEEE Transactions on Computers, c-18, 401-409. doi:10.1109/TC. 1969.222678
24. Saxena, A., & Kothari, M. (2007). Unsupervised approach for structure preserving dimensionality reduction. In Proceedings of the 6th International Conference on Advances in Pattern Recognition (ICAPR07) (pp. 315-318). Singapore: World Scientific Publishing Co. Pte. Ltd.
25. Setiono, R., & Liu, H. (1997, May). Neural-network feature selector. IEEE Transactions on Neural Networks, 8 (3), 654-662. doi:10.1109/72.572104
26. Siedlecki, W., & Sklansky, J. (1989). A note on genetic algorithms for large-scale feature selection. Pattern Recognition Letters, 10 (5), 335-347. doi:10.1016/0167-8655 (89) 90037-8
27. Sneath, P. H., & Sokal, R. R. (1973). Numerical taxonomy - The principles and practice of numerical classification. San Francisco: W. H. Freeman and Company.
28. Wei, H. L., & Billings, S. A. (2007). Feature subset selection and ranking for data dimensionality reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29 (1), 162-166. doi:10.1109/TPAMI.2007.250607 (Pubitemid 46415953)
29. Yan, S. C., Xu, D., Zhang, B. H. J., Yang, Q., & Lin, S. (2007). Graph embedding and extensions: A general framework for dimensionality reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29 (1), 40-51. doi:10.1109/TPAMI.2007.250598 (Pubitemid 46415944)
30. Yu, G. Z., Shao, S. H., Luo, B., & Zeng, X. H. (2009). A hybrid method for high-utility itemsets mining in large high-dimensional data. International Journal of Data Warehousing and Mining, 5 (1), 57-73.
31. Zhang, P., Verma, B., & Kumar, K. (2005, May). Neural vs. statistical classifier in conjunction with genetic algorithm based feature selection. Pattern Recognition Letters, 26 (7), 909-919. doi:10.1016/j. patrec.2004.09.053