A Heuristic - Statistical Feature Selection Criterion For Inductive Machine Learning In The Real World
Xiaojia Zhou, Tharam S. Dillon · 2005
Many machine learning methods have been developed for constructing decision trees from collections of examples. When they are applied to complicated real-world problems they often suffer from difficulties of coping with multi-valued or continuous features and noisy or conflicting data. To cope with these difficulties, a key issue is a powerful feature- selection criterion. After a brief review of the main existing criteria, this paper proposes a heuristic-statistical criterion symmetrical /spl tau/. This overcomes a number of the weaknesses of previous feature selection methods. Illustrative examples are presented.