Abstract
Many classification algorithms degrade their learning performance while irrelevant features are introduced. Feature selection is a process to choose an optimal subset of features and removes irrelevant ones. But many feature selection algorithms focus on filtering out the irrelevant attributes regarding the learned task only, not considering their hidden supportive information to other attributes: whether they are really irrelevant or potentially relevant? Since in medical domain, an irrelevant symptom is treated as the one providing neither explicit information nor supportive information for disease diagnosis. Therefore, the traditional feature selection methods may be unsuitable for handling such critical problem. In this paper, we propose a new method that selecting not only the relevant features, but also targeting at the latent useful irrelevant attributes by measuring their supportive importance to other attributes. The empirical results demonstrate a comparison of performance of various classification algorithms on twelve real-life datasets from UCI repository.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Liu, H., Motoda, H.: Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, Dordrecht (2000)
Molina, L.C., Belanche, L., Nebot, A.: Feature Selection Algorithms: A Survey and Experimental Evaluation. In: Proceedings of IEEE International Conference on Data Mining, ICDM, pp. 306–313. IEEE Computer Society Press, Los Alamitos (2002)
Dash, M., Liu, H.: Feature Selection for Classification. Intelligent Data Analysis 1(3), 131–156 (1997)
Miller, A.J.: Subset Selection in Regression. Chapman and Hall, Boca Raton (1990)
Devijver, P.A., Kittler, J.: Pattern Recognition: A Statistical Approach. Prentice-Hall International, Englewood Cliffs (1982)
John, G.H., Kohavi, R., Pfleger, K.: Irrelevant Features and the Subset Selection Problem. In: Proceedings of the Eleventh International Conference on Machine learning, pp. 121–129 (1994)
Molina, L.C., Belanche, L., Nebot, A.: Feature Selection Algorithms: A Survey and Experimental Evaluation. In: Proceedings of IEEE International Conference on Data Mining, ICDM, pp. 306–313. IEEE Computer Society Press, Los Alamitos (2002)
Guyon, I., Elisseeff, A.: An Introduction to Variable and Feature Selection. Journal of Machine Learning Research 3, 1157–1182 (2003)
Caruana, R., Sa, V.R.: Benefiting from the Variables that Variable Selection Discards. Journal of Machine Learning Research 3, 1245–1264 (2003)
The Hypertensive Research Group of Hear Internal Medicine Department of People’s Hospital of Beijing Medical University: Hundred Questions and Answers in Modern Knowledge of Hypertension (1998)
Jia, L., Xu, Y.: Guan Xing Bing De Zhen Duan Yu Zhi Liao. Jun Shi Yi Xue Ke Xue Chu Ban She (2001)
Pazzani, M.J.: Searching for Dependencies in Bayesian Classifiers. In: Proceedings of the Fifth International Workshop on AI and Statistics, pp. 424–429. Springer, Heidelberg (1996)
Zhu, X.L.: Fundamentals of Applied Information Theory. Tsinghua University Press, Beijing (2000)
Fayyad, U.M., Irani, K.B.: Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. In: Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pp. 1022–1027 (1993)
Kira, K., Rendell, L.: A Practical Approach to Feature Selection. In: Proceedings of International Conference on Machine Learning, Aberdeen, pp. 249–256. Morgan Kaufmann, San Francisco (1992a)
Kira, K., Rendell, L.: The Feature Selection Problem: Traditional Methods and New Algorithm. In: Proceedings of AAAI’92, San Jose, CA, AAAI Press, Menlo Park (1992b)
Jain, A., Zongker, D.: Feature Selection: Evaluation, Application and Small Sample Performance. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 153–158 (1997)
Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases. Department of Information and Computer Science, University of California (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Quinlan, J.R.: Induction of Decision Trees. Machine Learning 1, 81–106 (1986)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)
Quinlan, J.R.: Improved Use of Continuous Attributes in C4.5. Journal of Artificial Intelligence Research 4, 77–90 (1996)
Langley, P., Iba, W., Thompsom, K.: An Analysis of Bayesian Classifiers. In: Proceedings of the tenth national conference on artificial intelligence, pp. 223–228. AAAI Press, Menlo Park (1992)
Holte, R.C.: Very Simple Classification Rules Perform Well on Most Commonly Used Datasets. Machine Learning 11 (1993)
Vafaie, H., Imam, I.F.: Feature Selection Methods: Genetic Algorithms vs. Greedy like Search. In: Proceedings of International Conference on Fuzzy and Intelligent Control System (1994)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Chao, S., Li, Y., Dong, M. (2007). Supportive Utility of Irrelevant Features in Data Preprocessing. In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_42
Download citation
DOI: https://doi.org/10.1007/978-3-540-71701-0_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71700-3
Online ISBN: 978-3-540-71701-0
eBook Packages: Computer ScienceComputer Science (R0)