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Combination of Evidence-Based Classifiers for Text Categorization

Bi, Yaxin, Wu, Shengli, Wang, Hui and Guo, Guode (2011) Combination of Evidence-Based Classifiers for Text Categorization. In: Tools with Artificial Intelligence (ICTAI), 2011 23rd IEEE International Conference on. IEEE Press. 8 pp. [Conference contribution]

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Abstract

Abstract -- In this paper we propose an evidential fusion approach to combining the decisions of text classifiers. These text classifiers are generated by four widely used learning algorithms: Support Vector Machine (SVM), kNN (Nearest Neighbour), kNN model-based approach (kNNM), and Rocchio on two text corpora. We first model each classifier output as a list of prioritized decisions and then divide it into the subsets of 2 and 3 decisions which are subsequently represented by the evidential structures in terms of triplet and quartet. We also develop the general formulae based on the Dempster- Shafer theory of evidence for combining such decisions. To validate our method various experiments have been carried out over the data sets of 20-newsgroup and Reuters-21578, and a comparative analysis with an alternative dichotomous structure and with majority voting have also been conducted to demonstrate the advantage of our approach in combining text classifiers.

Item Type:Conference contribution (Paper)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:25472
Deposited By: Dr Yaxin Bi
Deposited On:19 Jan 2016 09:36
Last Modified:19 Jan 2016 09:36

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