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An ensemble method produces diverse classifiersand combines their decisions for ensemble’s decision. A numberof methods have been investigated for constructing ensemble inwhich some of them train classifiers with the generated patterns.This study investigates a new technique of training patterngeneration that is easy and effective for ensemble construction.The method modifies feature values of some patterns with thevalues of other patterns to generate different patterns fordifferent classifiers. The ensemble of decision trees based on theproposed technique was evaluated using a suite of 30benchmark classification problems, and was found to achieveperformance better than or competitive with relatedconventional methods. Furthermore, two different hybridensemble methods have been investigated incorporating theproposed technique of pattern generation with two popularensemble methods bagging and random subspace method(RSM). It is found that the performance of bagging and RSMalgorithms can be improved by incorporating feature valuesmodification with their training processes. Experimentalinvestigation of different types of modification techniques findsthat feature values modification with pattern values in the sameclass is better for generalization

Pattern Generation through Feature Values Modification and Decision Tree Ensemble Construction