Feature Ranking , Selection and Discretization

Many indices for evaluation of features have been considered. Applied to single features they allow for filtering irrelevant attributes. Algorithms for selection of subsets of features also remove redundant features. Hashing techniques enable efficient application of feature relevance indices to selection of feature subsets. A number of such methods have been applied to artificial and real-world data. Strong influence of continuous feature discretization and very good performance of separability-based discretization has been noted.

[1]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[2]  Hiroshi Motoda,et al.  Feature Extraction, Construction and Selection: A Data Mining Perspective , 1998 .

[3]  G. V. Kass.  An Exploratory Technique for Investigating Large Quantities of Categorical Data , 1980 .

[4]  Ramón López de Mántaras,et al.  A distance-based attribute selection measure for decision tree induction , 1991, Machine Learning.

[5]  Wlodzislaw Duch,et al.  A new methodology of extraction, optimization and application of crisp and fuzzy logical rules , 2001, IEEE Trans. Neural Networks.

[6]  J. Lorenzo,et al.  GD: A Measure Based on Information Theory for Attribute Selection , 1998, IBERAMIA.

[7]  Włodzisław Duch,et al.  Weighting and Selection of Features , 1999 .

[8]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[9]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[10]  Wlodzislaw Duch,et al.  Feature space mapping as a universal adaptive system , 1995 .

[11]  Hiroshi Motoda,et al.  Feature Selection for Knowledge Discovery and Data Mining , 1998, The Springer International Series in Engineering and Computer Science.

[12]  Wodzisaw Duch,et al.  THE SEPARABILITY OF SPLIT VALUE CRITERION , 2000 .

[13]  Mark A. Hall,et al.  Correlation-based Feature Selection for Machine Learning , 2003 .

[14]  Roberto Battiti,et al.  Using mutual information for selecting features in supervised neural net learning , 1994, IEEE Trans. Neural Networks.

[15]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[16]  D. V. Sridhar,et al.  Information theoretic subset selection for neural network models , 1998 .

[17]  Tomasz Winiarski,et al.  Feature selection based on information theory, consistency and separability indices , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..