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Mathematical Problems in Engineering
Volume 2015, Article ID 735014, 12 pages
http://dx.doi.org/10.1155/2015/735014
Research Article

Neighborhood Hypergraph Based Classification Algorithm for Incomplete Information System

Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Received 5 March 2015; Revised 18 May 2015; Accepted 21 May 2015

Academic Editor: Evangelos J. Sapountzakis

Copyright © 2015 Feng Hu and Jin Shi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The problem of classification in incomplete information system is a hot issue in intelligent information processing. Hypergraph is a new intelligent method for machine learning. However, it is hard to process the incomplete information system by the traditional hypergraph, which is due to two reasons: (1) the hyperedges are generated randomly in traditional hypergraph model; (2) the existing methods are unsuitable to deal with incomplete information system, for the sake of missing values in incomplete information system. In this paper, we propose a novel classification algorithm for incomplete information system based on hypergraph model and rough set theory. Firstly, we initialize the hypergraph. Second, we classify the training set by neighborhood hypergraph. Third, under the guidance of rough set, we replace the poor hyperedges. After that, we can obtain a good classifier. The proposed approach is tested on 15 data sets from UCI machine learning repository. Furthermore, it is compared with some existing methods, such as C4.5, SVM, NavieBayes, and NN. The experimental results show that the proposed algorithm has better performance via Precision, Recall, AUC, and -measure.