A Heuristic - Statistical Feature Selection Criterion For Inductive Machine Learning In The Real World

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.