C4.5: programs for machine learning
C4.5: programs for machine learning
Inductive Policy: The Pragmatics of Bias Selection
Machine Learning - Special issue on bias evaluation and selection
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Improving Text Classification by Shrinkage in a Hierarchy of Classes
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Journal of Artificial Intelligence Research
Using concept taxonomies for effective tree induction
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part II
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This paper describes the use of taxonomic hierarchies of conceptclasses (dependent class values) for knowledge discovery The approach allows evidence to accumulate for rules at different levels of generality and avoids the need for domain experts to predetermine which levels of concepts should be learned In particular, higher-level rules can be learned automatically when the data doesn't support more specific learning, and higher level rules can be used to predict a particular case when the data is not detailed enough for a more specific rule The process introduces difficulties concerning how to heuristically select rules during the learning process, since accuracy alone is not adequate This paper explains the algorithm for using concept-class taxonomies, as well as techniques for incorporating granularity (together with accuracy) in the heuristic selection process Empirical results on three data sets are summarized to highlight the tradeoff between predictive accuracy and predictive granularity.