ICTACT Journal on Soft Computing

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Enrichment of Ensemble Learning using K-Modes Random Sampling


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1 Department of Computer Applications, Madurai Kamaraj University, India
     

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Ensemble of classifiers combines the more than one prediction models of classifiers into single model for classifying the new instances. Unbiased samples could help the ensemble classifiers to build the efficient prediction model. Existing sampling techniques fails to give the unbiased samples. To overcome this problem, the paper introduces a k-modes random sample technique which combines the k-modes cluster algorithm and simple random sampling technique to take the sample from the dataset. In this paper, the impact of random sampling technique in the Ensemble learning algorithm is shown. Random selection was done properly by using k-modes random sampling technique. Hence, sample will reflect the characteristics of entire dataset.

Keywords

Sampling, Ensemble Classifiers, Cluster Random Sample.
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  • Enrichment of Ensemble Learning using K-Modes Random Sampling

Abstract Views: 246  |  PDF Views: 4

Authors

Balamurugan Mahalingam
Department of Computer Applications, Madurai Kamaraj University, India
S. Kannan
Department of Computer Applications, Madurai Kamaraj University, India
Vairaprakash Gurusamy
Department of Computer Applications, Madurai Kamaraj University, India

Abstract


Ensemble of classifiers combines the more than one prediction models of classifiers into single model for classifying the new instances. Unbiased samples could help the ensemble classifiers to build the efficient prediction model. Existing sampling techniques fails to give the unbiased samples. To overcome this problem, the paper introduces a k-modes random sample technique which combines the k-modes cluster algorithm and simple random sampling technique to take the sample from the dataset. In this paper, the impact of random sampling technique in the Ensemble learning algorithm is shown. Random selection was done properly by using k-modes random sampling technique. Hence, sample will reflect the characteristics of entire dataset.

Keywords


Sampling, Ensemble Classifiers, Cluster Random Sample.

References