在資料探勘的領域中，分類問題是最常被探討的議題之一。當資料的特徵(屬性)及類別已知時，分類會依特徵的特性以機器學習的方式建立預測模型，進而對還未分類的資料進行歸類。然而對於大多數的分類問題來說，特徵選取是一項很重要的前置作業。特徵選取的主要目的是刪除多餘或不重要的特徵，以降低特徵的維度及計算的複雜性，並增加分類的準確率。
目前特徵選取的演算方法主要可分為過濾法及包裝法。過濾法憑藉自身的演算法去評估資料特徵的特性，所求得的特徵子集合再交由分類演算法做評估；包裝法則直接採用分類演算法所求得的分類準確率去挑選出特徵子集合。一般來說，包裝法的分類效果較優於過濾法，但在計算上卻較為耗時。因此本研究將提出一種以直交表為特徵選取技術並結合支持向量機的包裝演算法，依據直交表的特性而做出系統化的選取規則，並期望能降低演算的時間及提高分類的準確率。最後我們將利用UCI資料庫中八筆分類問題的資料進行實驗，證實出所提的特徵選取技術能有效的刪除掉不必要或多餘的特徵，進而增加分類的準確率。

[1] G.H.John,R. Kohavi, and K. Pfleger, “Irrelevant features and the subset election problem”, Machine Learning: Proceedings of the Eleventh International Conference, 121–129, 1994.
[2] B. Baesens et al, “Benchmarking state-of-the-art classification algorithms for credit scoring”, Journal of the Operational Research Society, 54: 627–635, 2003.
[3] J. R. Quinlan, “Discovering rules by induction from large collections of examples”, Expert Systems in the Micro-electronic Age, 1979
[4] J. R. Quinlan, “Induction of decision trees. Machine Learning”,1(1):81-106,1986.
[5]J. R. Quinlan,“C4.5: Programs for Machine Learning”, Morgan Kaufmann, San Mateo, California,1993.
[6] L. Breiman, J. H. Friedman, R. A. Olshen and C. J. Stone, “Classifications and Regression Trees”, Wadsworth, Pacific Grove, California, USA, 1984
[7] J. N. Morgan and J. A. Sonquist,“ Problems in the analysis of survey data, and a proposal”, Journal of the American Statistical Association, 58:415-434, 1963.
[8] G. V. Kass, “An Exploratory Technique for Investigating Large Quantities of Categorical Data”, Applied Statistics, 29(2): 119-127, 1980.
[9] P. Clark and T. Niblett, “ The CN2 induction algorithm”. Machine Learning, 3:261–283, 1989.
[10] P. Langley, W. Iba and K. Thompson, “An analysis of Bayesian classifiers”, Proceedings of the Tenth National Conference on Artificial Intelligence, 223–228, 1992.
[11] P. Domingos and M. Pazzani, “On the optimality of the simple Bayesian classifier under zero-one loss”, Machine Learning, 29:103–130, 1997
[12] I. Rish, “An empirical study of the naive Bayes classifier”, In Proceedings of IJCAI-01 workshop on Empirical Methods in AI, 41--46, 2001
[13] Z. Zhu, Y. S. Ong and M. Dash, “Wrapper-Filter Feature Selection Algorithm
Using A Memetic Framework”, IEEE Trans. Syst. Man Cybern. Part B 37 (1):70–76, 2007
[14] E. Fix and J. L. Hodges, Jr., “Discriminatory analysis, nonparametric discrimination: Consistency Properties”, International Statistical Review / Revue Internationale de Statistique, 57(3):238-247, 1989
[15] S. A. Dudani, “The distance-weighted k-nearest-neighbor rule,” IEEE Trans.
Syst. Man Cyber, 6:325–327, 1976.
[16] D. E. Rumelhart, D. E. Hinton and R. J. Williams, “Learning Internal Representations by Error Propagation in Parallel Distribution Distributed Processing”,
MIT Press, Cambridge, MA, 318-362, 1986
[17] V. Vapnik, “The Nature of Statistical Learning Theory”, Springer, New York, 1995
[18] V. Vapnik, “Statistical Learning Theory”, Wiley, New York, 1998
[19] S. Tong and D. Koller, “Support vector machine active learning with applications to text classification”, Journal of Machine Learning Research, 45-66, 2001
[20] H. Lodhi, J. Shawe-Taylor, N. Christianini and C. Watkins, “Text classification using string kernels”, In Advances in Neural Information Processing Systems, 13, 2001.
[21] C. J.C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition”, Data Mining and Knowledge Discovery, 2:121–167, 1998
[22]C. Papageorgiou, T. Evgeniou and T. Poggio, “A trainable pedestrian detection system”, IEEE Conference on Intelligent Vehicles, 1998
[23] E. Osuna, R. Freund and F. Girosi, “Training support vector machines: An application to face detection”, Computer Vision and Pattern Recognition, 1997.
[24] P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features”, Computer Vision and Pattern Recognition, 2001
[25]E. Byvatov and G. Schneider, “Support vector machine applications in bioinformatics”, Appl Bioinformatics, 2(2):67-77, 2003
[26] T. Furey, N. Cristianini, N. Duffy, D. W. Bednarski, M. Schummer and D. Haussler, “Support vector machine classification and validation of cancer tissue samples using microarray expression data”, Bioinformatics, 16:906–914, 2000
[27] A. Asuncion and D. Newman, “UCI Machine Learning Repository”, 2007, http://www.ics.uci.edu/∼mlearn/MLRepository.html.
[28] M. Dash and H. Liu, “feature selection for classification”, Department of Information Systems & Computer Science, 131–156, 1997
[29] A. Blum and P. Langley, “Selection of relevant features and examples in machine learning”, Artificial Intelligence, 97:245–271,1997.
[30] L. Rokach, B. Chizi and O. Maimon, “A methodology for improving the performance of non-ranker feature selection filters,” International Journal of Recognition and Artificial Intelligence, 21(5): 809-830, 2007
[31] D. Mladenić, “Feature Selection for Dimensionality Reduction”, Computer science, 84-102, 2006.