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Classification learning using all rules
[chapter]
1998
Lecture Notes in Computer Science
The covering algorithm has been ubiquitous in the induction of classification rules. This approach to machine learning uses heuristic search that seeks to find a minimum number of rules that adequately explain the data. However, recent research has provided evidence that learning redundant classifiers can increase predictive accuracy. Learning all possible classifiers seems to be a plausible ultimate form of this notion of redundant classifiers. This paper presents an algorithm that in effect
doi:10.1007/bfb0026685
fatcat:lvpmd653fjhfvpuhqqdfgoskqu