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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 4953280, 11 pages
Research Article

A Method for Entity Resolution in High Dimensional Data Using Ensemble Classifiers

1PLA University of Science and Technology, Nanjing, Jiangsu 210007, China
2Nanjing Telecommunication Technology Institute, Nanjing, Jiangsu 210007, China

Correspondence should be addressed to Cao Jian-jun

Received 31 October 2016; Accepted 17 January 2017; Published 15 February 2017

Academic Editor: Yaguo Lei

Copyright © 2017 Liu Yi et al. 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.


In order to improve utilization rate of high dimensional data features, an ensemble learning method based on feature selection for entity resolution is developed. Entity resolution is regarded as a binary classification problem, an optimization model is designed to maximize each classifier’s classification accuracy and dissimilarity between classifiers and minimize cardinality of features. A modified multiobjective ant colony optimization algorithm is employed to solve the model for each base classifier, two pheromone matrices are set up, weighted product method is applied to aggregate values of two pheromone matrices, and feature’s Fisher discriminant rate of records’ similarity vector is calculated as heuristic information. A solution which is called complementary subset is selected from Pareto archive according to the descending order of three objectives to train the given base classifier. After training all base classifiers, their classification outputs are aggregated by max-wins voting method to obtain the ensemble classifiers’ final result. A simulation experiment is carried out on three classical datasets. The results show the effectiveness of our method, as well as a better performance compared with the other two methods.