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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.6 No.3
- 발행연도
- 2017.6
- 수록면
- 183 - 192 (10page)
이용수
초록· 키워드
오류제보하기
k-nearest neighbor (K-NN) is a well-known classification algorithm, being feature space–based on nearest-neighbor training examples in machine learning. However, K-NN, as we know, is a lazy learning method. Therefore, if a K-NN–based system very much depends on a huge amount of history data to achieve an accurate prediction result for a particular task, it gradually faces a processing-time performance-degradation problem. We have noticed that many researchers usually contemplate only classification accuracy. But estimation speed also plays an essential role in realtime prediction systems. To compensate for this weakness, this paper proposes correlation coefficient–based clustering (CCC) aimed at upgrading the performance of K-NN by leveraging processing-time speed and plurality rule–based density (PRD) to improve estimation accuracy. For experiments, we used real datasets (on breast cancer, breast tissue, heart, and the iris) from the University of California, Irvine (UCI) machine learning repository. Moreover, real traffic data collected from Ojana Junction, Route 58, Okinawa, Japan, was also utilized to lay bare the efficiency of this method. By using these datasets, we proved better processing-time performance with the new approach by comparing it with classical K-NN. Besides, via experiments on realworld datasets, we compared the prediction accuracy of our approach with density peaks clustering based on K-NN and principal component analysis (DPC-KNN-PCA).
목차
Abstract
1. Introduction
2. Related Work
3. Proposed Correlation Coefficient–based Clustering and Plurality Rule–based Density for K-NN
4. Experimentation
5. Conclusion
References
참고문헌 (7)
참고문헌 신청
B.Tang and H.He, Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA, “Enn: Extended Nearest Neighbor Method for pattern Recognition”, IEEE Computational Intelligence Magazine, 1556- 603X, August 2015. Article (CrossRef Link)
K.Chomboon, P.Chujai, P.Teerarassamee, K.Kerdprasop and N.Kerdprasop, “An Empirical Study of Distance Metrics for k-Nearest Neighbor Algorithm”, Proceedings of the 3rd International Conference on Industrial Application Engineering, 2015. Article (CrossRef Link)
Z.Nazari and D.Kang, “Denisty Based Support Vector Machines for Classification”, International Journal of Advanced Research in Artificial Intelligence(IJARAI), Vol. 4, N0.4, 2015. Article (CrossRef Link)
M.Du,S.Ding and H.Jia, School of Computer Science and Technology, China University of Mining and Technology, “Study on density peaks clustering based on k-nearest neighbors and principal component analysis”, Knowledge-Based Systems Journal, Knowledge-Bawd System 99 (2016) 135- 145. Article (CrossRef Link)
M.Doshi and S.K.Chaturvedi, “Correlation Based Feature Selection Technique to Predict Student Performance”, International Journal of Computer Networks & Communications (IJCNC) Vol.6, No.3, May 2014. Article (CrossRef Link)
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