|
|
|
| k-nearest Neighbor Classification Based on Influence Function |
| Zhi Wei-mei Zhang Ting Fan Ming |
| (College of Information Engineering, Zhengzhou University, Zhengzhou 450052, China) |
|
|
|
|
Abstract Classification is a supervised learning. It determines the class label of an unlabeled instance by learning model based on the training dataset. Unlike traditional classification, this paper views classification problem from another perspective, that is influential function. That is, the class label of an unlabeled instance is determined by the influence of the training data set. Firstly, the idea of classification is introduced based on influence function. Secondly, the definition of influence function is given and three influence functions are designed. Finally, this paper proposes k-nearest neighbor classification method based on these three influence functions and applies it to the classification of imbalanced data sets. The experimental results on 18 UCI data sets show that the proposed method improves effectively the k-nearest neighbor generalization ability. Besides, the proposed method is effective for imbalanced classification.
|
|
Received: 13 November 2014
Published: 02 June 2015
|
|
|
|
Corresponding Authors:
Zhi Wei-mei
E-mail: iewmzhi@zzu.edu.cn
|
|
|
|
| [1] |
Tan P N and Steinbach M著, 范明, 范宏建, 译. 数据挖掘入门[M]. 第2版, 北京: 人民邮电出版社, 2011: 127-187.
|
| [2] |
Quinlan J S. Induction of decision trees[J]. Machine Learning, 1986, 1(1): 81-106.
|
| [3] |
Domingos P and Pazzani M J. Beyond independence: conditions for the optimality of the simple bayesian classifier[C].?Proceedings of the International Conference on Machine Learning, Bari, Italy, 1996: 105-112.
|
| [4] |
Rumelhart D E, Hinton G E, and Williams R J. Learning representations by back-propagating errors[J]. Nature, 1986, 323(9): 533-536.
|
| [5] |
Boser B E,?Guyon I M, and Vapnik V N.?A training algorithm for optimal margin classifiers[C].?Proceedings of the Conference on Learning Theory, Pittsburgh, USA, 1992: 144-152.
|
| [6] |
Dasarathy B V. Nearest Neighbor (NN) norms: NN Pattern Classification Techniques[M]. Michigan: IEEE Computer Society Press, 1991: 64-85.
|
| [7] |
Leake D B.?Experience, introspection and expertise: learning to refine the case-based reasoning process[J].?Journal of Experimental & Theoretical Artificial Intelligent, 1996, 8(3/4): 319-339.
|
| [8] |
Hinneburg A and Keim D A. An efficient approach to clustering in large multimedia databases with noise[C]. Proceedings of the Knowledge Discovery and Data Mining, New York, USA, 1998: 58-65.
|
| [9] |
Bache K and Lichman M. UCI repository of machine learning databases[OL]. http://www.ics.uci.edu/~mlearn/MLRepository.
|
|
html. 2014.5.
|
| [10] |
Liu X Y, Li Q Q, and Zhou Z H. Learning imbalanced multi-class data with optimal dichotomy weights[C]. Proceedings of the 13th IEEE International Conference on Data Mining, Dallas, USA, 2013: 478-487.
|
| [11] |
He H B and Edwardo A G. Learning from imbalanced Data [J]. IEEE Transactions on Knowledge and Data Engineering, 2009, 21(9): 1263-1284.
|
| [12] |
Maratea A, Petrosino A, and Manzo M. Adjusted F-measure and kernel scaling for imbalanced data learning[J]. Information Sciences, 2014(257): 331-341.
|
| [13] |
Wang S and Yao X. Multiclass imbalance problems: analysis and potential solutions[J]. IEEE Transactions on Systems, Man and Cybernetics, Part B, 2012, 42(4): 1119-1130.
|
| [14] |
Lin M, Tang K, and Yao X. Dynamic sampling approach to training neural networks for multiclass imbalance classification[J]. IEEE Transactions on Neural Networks and Learning Systems, 2013, 24(4): 647-660.
|
| [15] |
Peng L Z, Zhang H L, Yang B, et al.. A new approach for imbalanced data classification based on data gravitation[J]. Information Sciences, 2014(288): 347-373.
|
| [16] |
Menardi G and Torelli N. Training and assessing classification rules with imbalanced data[J]. Data Mining and Knowledge Discovery, 2014, 28(1): 92-122.
|
|
|
|
