Empirical Evaluation of Feature Subset Selection Based on a Real-World Data Set

Authors:
Petra Perner;Chidanand Apté
Affiliations:
-;-
Venue:
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Year:
2000

Citing 5
Cited 1

Introduction to statistical pattern recognition (2nd ed.)

Introduction to statistical pattern recognition (2nd ed.)
A Distance-Based Attribute Selection Measure for Decision Tree Induction

Machine Learning
C4.5: programs for machine learning

C4.5: programs for machine learning
Technical Note: Bias in Information-Based Measures in Decision Tree Induction

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Floating search methods in feature selection

Pattern Recognition Letters

Feature selection in an electric billing database considering attribute inter-dependencies

ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining

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Abstract

Selecting the right set of features for classification is one of the most important problems in designing a good classifier. Decision tree induction algorithms such as C4.5 have incorporated in their learning phase an automatic feature selection strategy while some other statistical classification algorithm require the feature subset to be selected in a preprocessing phase. It is well know that correlated and irrelevant features may degrade the performance of the C4.5 algorithm. In our study, we evaluated the influence of feature preselection on the prediction accuracy of C4.5 using a real-world data set. We observed that accuracy of the C4.5 classifier can be improved with an appropriate feature preselection phase for the learning algorithm.