文章基本信息

标题：An Improved Ensemble Classifier Results with Consideration of Classifier Importance
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作者：C. Gayathri ; R.Umarani
期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2016
卷号：36
期号：2
页码：96-100
DOI：10.14445/22312803/IJCTT-V36P117
出版社：Seventh Sense Research Group
摘要：Financial fraud detection is the most concerned and complicated task in the real world environment which would requires complete and structured financial data set for accurate detection. These issues are resolved in our previous work namely Optimal Ensemble Architecture Selection using FFA (OEACFFA). This work would select the most optimal ensemble classifier architecture for the accurate detection financial fraud behaviour by changing the ensemble architecture of different combination of base classifiers and subset features in every iteration. This methodology is proved to be better in the accurate classification result. OEACFFA makes use of weighted average methodology for obtaining the final classification result from the ensemble classifier. However the weighted average method cannot produce an accurate ensemble result where the classifiers that are not provided with equal importance. In real world environment, classifier should be differentiated in importance based on their classification result. This problem is resolved in this research work by introducing the novel approach called the Dempster Shafer based Ensemble Classifier method (DSECM). The dempster shafer theory can give better ensemble classifier result than the weighted average method in presence of different importance values of classifiers too. This approach would retrieve the ensemble result as the base classifier result which is having more confidence in terms of their weight. The experimental tests were conducted in the matlab simulation environment and the evaluation is done by comparing it with the OEAC_FFA which is based on weighted average method. This is proved that the DSECM approach is better than the other mechanisms.
关键词：Financial Fraud detection; Fusion; Dempster shafer; confidence score.