Data Mining and Knowledge Engineering

Abstract

In this research the performance of AdaBoost algorithm and Chaos Genetic Algorithm were made in real learning problems. To cluster the data two experiments were made, initially the experiment were made to compare the AdaBoost with Chaos Genetic Algorithm when used to aggregate various classifiers. Secondly the Time-Cost Tradeoff (TCT) problems have been studied with the optimization techniques. With higher efficiency the two methods were compared and processed.  It avoids local convergence when comparing the diversity of Chaos Genetic Algorithm with AdaBoost. Moreover, the experimental results shows that the proposed method minimizes the number of iterations in optimization problems and significantly increase the performance of the adaboost. This paper compared the performance of these two methods by using the machine learning benchmarks.

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