vioft2nntf2t|tblJournal|Abstract_paper|0xf4ffef8e1f000000878f010001000500 In Ensemble classifiers, the Combination of multiple prediction models of classifiers is important for making progress in a variety of difficult prediction problems. Ensemble of classifiers proved potential in getting higher accuracy compared to single classifier. Even though by the usage ensemble classifiers, still there is in-need to improve its performance. There are many possible ways available to increase the performance of ensemble classifiers. One of the ways is sampling, which plays a major role for improving the quality of ensemble classifier. Since, it helps in reducing the bias in input data set of ensemble. Sampling is the process of extracting the subset of samples from the original dataset. In this research work, analysis is done on sampling techniques for ensemble classifiers. In ensemble classifier, specifically one of the probability based sampling techniques is being always used. Samples are gathered in a process which gives all the individuals in the population of equal chances, such that, sampling bias is removed. In this paper, analyse the performance of ensemble classifiers by using various sampling techniques and list out their drawbacks.
M. Balamurugan, S. Kannan
Madurai Kamaraj University, India
Ensemble of Classifiers, Sampling, Random Forest, Boosting
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 6 , Issue: 4 , Pages: 1293-1296 )
Date of Publication :
July 2016
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