Boolean Feature Discovery in Empirical Learning
Machine Learning
Learning from examples—a uniform view
International Journal of Man-Machine Studies
Rule induction with CN2: some recent improvements
EWSL-91 Proceedings of the European working session on learning on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
CSC '93 Proceedings of the 1993 ACM conference on Computer science
Knowledge Acquisition from Databases
Knowledge Acquisition from Databases
Incremental Induction of Decision Trees
Machine Learning
Machine Learning
A Mathematical Theory of Communication
A Mathematical Theory of Communication
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In most data mining applications where induction is used as the primary tool for knowledge extraction, it is difficult to precisely identify a complete set of relevant attributes. The real world database from which knowledge is to be extracted usually contains a combination of relevant, noisy and irrelevant attributes. Therefore, pre-processing the database to select relevant attributes becomes a very important task in knowledge discovery and data mining. This paper starts with two existing induction systems, C4.5 and HCV, and uses one of them to select relevant attributes for the other. Experimental results on 12 standard data sets showtha t using HCV induction for C4.5 attribute selection is generally useful.