A Method for Entity Resolution in High Dimensional Data Using Ensemble Classifiers
Joint Authors
Xing, Zhou
Xingchun, Diao
Jianjun, Cao
Yi, Liu
Jian-jun, Cao
Source
Mathematical Problems in Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-02-15
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Yi, Liu& Xingchun, Diao& Jian-jun, Cao& Xing, Zhou& Jianjun, Cao. 2017. A Method for Entity Resolution in High Dimensional Data Using Ensemble Classifiers. Mathematical Problems in Engineering،Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1190566
Modern Language Association (MLA)
Yi, Liu…[et al.]. A Method for Entity Resolution in High Dimensional Data Using Ensemble Classifiers. Mathematical Problems in Engineering No. 2017 (2017), pp.1-11.
https://search.emarefa.net/detail/BIM-1190566
American Medical Association (AMA)
Yi, Liu& Xingchun, Diao& Jian-jun, Cao& Xing, Zhou& Jianjun, Cao. A Method for Entity Resolution in High Dimensional Data Using Ensemble Classifiers. Mathematical Problems in Engineering. 2017. Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1190566
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1190566