Kernel Classification Using Data Distribution and Soft Decision MCT-Adaboost


KIPS Transactions on Software and Data Engineering, Vol. 6, No. 3, pp. 149-154, Mar. 2017
10.3745/KTSDE.2017.6.3.149,   PDF Download:
Keywords: Soft Decision, Kernel Classification, Data Distribution
Abstract

The MCT-Adaboost algorithm chooses an optimal set of features in each rounds. On each round, it chooses the best feature by calculate minimizing error rate using feature index and MCT kernel distribution. The involved process of weak classification executed by a hard decision. This decision occurs some problems when it chooses ambiguous kernel feature. In this paper, we propose the modified MCT-Adaboost classification using soft decision. The typical MCT-Adaboost assigns a same initial weights to each datum. This is because, they assume that all information of database is blind. We assign different initial weights with our propose new algorithm using some statistical properties of involved features. In experimental results, we confirm that our method shows better performance than the traditional one.


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Cite this article
[IEEE Style]
K. Kim and H. Choi, "Kernel Classification Using Data Distribution and Soft Decision MCT-Adaboost," KIPS Transactions on Software and Data Engineering, vol. 6, no. 3, pp. 149-154, 2017. DOI: 10.3745/KTSDE.2017.6.3.149.

[ACM Style]
Kisang Kim and Hyung-Il Choi. 2017. Kernel Classification Using Data Distribution and Soft Decision MCT-Adaboost. KIPS Transactions on Software and Data Engineering, 6, 3, (2017), 149-154. DOI: 10.3745/KTSDE.2017.6.3.149.