A Novel EPA-KNN Gene Classification Algorithm

Authors:
Haijun Wang;Yaping Lin;Xinguo Lu;Yalin Nie
Affiliations:
School of Computer and Communication, Hunan University, Changsha 410082, China and School of Science, Henan University of Science and Technology, Luoyang 471003, China;School of Computer and Communication, Hunan University, Changsha 410082, China and School of Software, Hunan University, Changsha 410082, China;School of Computer and Communication, Hunan University, Changsha 410082, China;School of Computer and Communication, Hunan University, Changsha 410082, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

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Abstract

Accurate classification of samples using gene expression frofiles is very important in cancer detection and treatment. In this paper, a novel EPA-KNN (Emerging Patterns Advanced-K Nearest Neighbors) gene classification algorithm is proposed. Bayes estimation is applied for the computation of entropy to improve its reliability, and RCP (Random Cut Point) is presented to strengthen the generalization about unknown test samples. With these improvements, the EPAs are acquired. Then an EPA based classifier is constructed inspired by KNN. The experimental results show the new algorithm is feasible and effective.