Abstract
Feature selection is one of key issues related with data pre-processing of classification task in a data mining process. Although many efforts have been done to improve typical feature selection algorithms (FSAs), such as filter methods and wrapper methods, it is hard for just one FSA to manage its performances to various datasets. To above problems, we propose another way to support feature selection procedure, constructing proper FSAs to each given dataset. Here is discussed constructive meta-level feature selection that re-constructs proper FSAs with a method repository every given datasets, de-composing representative FSAs into methods. After implementing the constructive meta-level feature selection system, we show how constructive meta-level feature selection goes well with 32 UCI common data sets, comparing with typical FSAs on their accuracies. As the result, our system shows the highest performance on accuracies and the availability to construct a proper FSA to each given data set automatically.
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.
Similar content being viewed by others
References
Komori, M., Abe, H., Yamaguchi, T.: A new feature selection method based on dynamic inclemental extension of seed features. In: Proceedings of Knowledge-Based Software Engineering, pp. 291–296 (2002)
John, G.H., Kohavi, R., Pfleger, K.: Irrelevant features and the subset selection problem. In: International Conference on Machine Learning, pp. 121–129 (1994)
John, G.H.: Enhancements to the data mining process. PhD thesis, Computer Science Department, Stanford University (1997)
Liu, H., Motoda, H.: Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, Dordrecht (1998)
Hall, M.A.: Benchmarking attribute selection techniques for data mining. Technical Report Working Paper 00/10, Department of Computer Science, University of Waikato (2000)
Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence 97, 273–324 (1997)
Kira, K., Rendell, L.: A practical approach to feature selection. In: Sleeman, D., Edwards, P. (eds.) Proceedings of the Ninth International Conference on Machine Learning, pp. 249–256 (1992)
Kononenko, I.: Estimating attributes: Analysis and extensions of relief. In: Proceedings of the 1994 European Conference on Machine Learning, pp. 171–182 (1994)
Alumualim, H., Dietterich, T.G.: Learning boolean concepts in the presence of many irrelevant features. Artificial Intelligence 69, 279–305 (1994)
Hall, M.: Correlation-based Feature Selection for Machine Learning. PhD thesis, Department of Computer Science, University of Waikato (1998)
Quinlan, J.R.: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1992)
Langley, P.: Selection of relevant features in machine learning. In: Proceedings of the AAAI Fall Symposium on Relevance (1994)
Molina, L.C., Beranche, L., Nebot, A.: Feature selection algorithms: A survey and experimental evaluation. In: Proceedings of the 2002 Internatiolan Conference on Data Mining, pp. 306–313 (2002)
Witten, I., Frank, E.: Data Mining: Practical machine learning tools and techniques with Java implementations. Morgan Kaufmann, San Francisco (2000)
Mierswa, I., Klinkenberg, R., Fischer, S., Ritthoff, O.: A Flexible Platform for Knowledge Discovery Experiments: YALE – Yet Another Learning Environment. In: LLWA 2003 - Tagungsband der GI-Workshop-Woche Lernen - Lehren – Wissen - Adaptivität (2003)
Breiman, L.: Bagging predictors. Machine Learning 24, 123–140 (1996)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Proceedings the Second European Conference on Computational Learning Theory (1995)
Wolpert, D.: Stacked generalization. Neural Network 5, 241–260 (1992)
Gama, J., Brazdil, P.: Cascade generalization. Machine Learning 41, 315–343 (2000)
METAL (2002), http://www.metal-kdd.org/
Bernstein, A., Provost, F.: An intelligent assistant for knowledge discovery process. In: IJCAI 2001 Workshop on Wrappers for Performance Enhancement in KDD (2001)
Abe, H., Yamaguchi, T.: Constructive meta-learning with machine learning method repositories. In: Proceedings of the seventeenth International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, pp. 502–511 (2004)
Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Vafaie, H., Jong, K.D.: Genetic algorithms as a tool for feature selection in machine learning. In: Proceedings of the fourth International Conference on Tools with Artificial Intelligence, pp. 200–204 (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Abe, H., Yamaguchi, T. (2006). Constructive Meta-level Feature Selection Method Based on Method Repositories. In: Ng, WK., Kitsuregawa, M., Li, J., Chang, K. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2006. Lecture Notes in Computer Science(), vol 3918. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731139_11
Download citation
DOI: https://doi.org/10.1007/11731139_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33206-0
Online ISBN: 978-3-540-33207-7
eBook Packages: Computer ScienceComputer Science (R0)