Combination of Evidence-Based Classifiers for Text Categorization

Yaxin Bi, Shengli Wu, Hui Wang, Guode Guo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

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.
LanguageEnglish
Title of host publicationUnknown Host Publication
Number of pages8
Publication statusPublished - 2011
EventTools with Artificial Intelligence (ICTAI), 2011 23rd IEEE International Conference on -
Duration: 1 Jan 2011 → …

Conference

ConferenceTools with Artificial Intelligence (ICTAI), 2011 23rd IEEE International Conference on
Period1/01/11 → …

Fingerprint

Classifiers
Learning algorithms
Support vector machines
Fusion reactions
Experiments

Cite this

Bi, Y., Wu, S., Wang, H., & Guo, G. (2011). Combination of Evidence-Based Classifiers for Text Categorization. In Unknown Host Publication
Bi, Yaxin ; Wu, Shengli ; Wang, Hui ; Guo, Guode. / Combination of Evidence-Based Classifiers for Text Categorization. Unknown Host Publication. 2011.
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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.",
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Bi, Y, Wu, S, Wang, H & Guo, G 2011, Combination of Evidence-Based Classifiers for Text Categorization. in Unknown Host Publication. Tools with Artificial Intelligence (ICTAI), 2011 23rd IEEE International Conference on, 1/01/11.

Combination of Evidence-Based Classifiers for Text Categorization. / Bi, Yaxin; Wu, Shengli; Wang, Hui; Guo, Guode.

Unknown Host Publication. 2011.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Combination of Evidence-Based Classifiers for Text Categorization

AU - Bi, Yaxin

AU - Wu, Shengli

AU - Wang, Hui

AU - Guo, Guode

PY - 2011

Y1 - 2011

N2 - 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.

AB - 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.

M3 - Conference contribution

BT - Unknown Host Publication

ER -

Bi Y, Wu S, Wang H, Guo G. Combination of Evidence-Based Classifiers for Text Categorization. In Unknown Host Publication. 2011