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Reseach Article

Hybrid Genetic and Dempster Shafer Theory based Classifiers for Multi-Class Classification Tasks

by Jitendra Patel, Anurag Jain
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 137 - Number 2
Year of Publication: 2016
Authors: Jitendra Patel, Anurag Jain
10.5120/ijca2016908679

Jitendra Patel, Anurag Jain . Hybrid Genetic and Dempster Shafer Theory based Classifiers for Multi-Class Classification Tasks. International Journal of Computer Applications. 137, 2 ( March 2016), 5-9. DOI=10.5120/ijca2016908679

@article{ 10.5120/ijca2016908679,
author = { Jitendra Patel, Anurag Jain },
title = { Hybrid Genetic and Dempster Shafer Theory based Classifiers for Multi-Class Classification Tasks },
journal = { International Journal of Computer Applications },
issue_date = { March 2016 },
volume = { 137 },
number = { 2 },
month = { March },
year = { 2016 },
issn = { 0975-8887 },
pages = { 5-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume137/number2/24245-2016908679/ },
doi = { 10.5120/ijca2016908679 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:37:14.539829+05:30
%A Jitendra Patel
%A Anurag Jain
%T Hybrid Genetic and Dempster Shafer Theory based Classifiers for Multi-Class Classification Tasks
%J International Journal of Computer Applications
%@ 0975-8887
%V 137
%N 2
%P 5-9
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Classification is a data exploration and learning mechanism, which has been widely studied and a wide range of applications subject. Supervised Classification is based on association rules and if we increase number of association rule degree of accuracy of classification is also being increase but larger number of rule take longer time to classify. Recently researcher is focus to develop an model that increase the accuracy in minimum time. In this paper Genetic based multi class classification model is proposed. Proposed model also use Dumpster shafer theorem for confining resultant rule set generated by GA algorithm. This paper used wine data set available at UCI machine learning website for classification and applies 3 cross fold mechanism for cross validation.

References
  1. Dewan Md. Farid, Li Zhang, Chowdhury Mofizur Rahman, M.A. Hossain, Rebecca Strachan, “Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks”, Expert Systems with Applications, Volume 41, Issue 4, Part 2, Pages 1937-1946, March 2014
  2. Seokho Kang, Sungzoon Cho, Pilsung Kang, Multi-class classification via heterogeneous ensemble of one-class classifiers, Engineering Applications of Artificial Intelligence, Volume 43, August 2015, Pages 35-43
  3. Joshi, S.; Nigam, B., "Categorizing the Document Using Multi Class Classification in Data Mining," in Computational Intelligence and Communication Networks (CICN), 2011 International Conference on , vol., no., pp.251-255, 7-9 Oct. 2011
  4. Cholissodin, I.; Kurniawati, M.; Indriati; Arwani, I., "Classification of campus e-complaint documents using Directed Acyclic Graph Multi-class SVM based on analytic hierarchy process," in Advanced Computer Science and Information Systems (ICACSIS), 2014 International Conference on , vol., no., pp.247-253, 18-19 Oct. 2014
  5. Ahmed, M.S.; Khan, L.; Rajeswari, M., "Using Correlation Based Subspace Clustering for Multi-label Text Data Classification," in Tools with Artificial Intelligence (ICTAI), 2010 22nd IEEE International Conference on , vol.2, no., pp.296-303, 27-29 Oct. 2010
  6. Yingqin Gu1,2, Hongyan Liu3, Jun He1,2, Bo Hu1,2 and Xiaoyong Du1,2 “A Multi-relational Classification Algorithm based on Association Rules” pp.4-9 2009 IEEE.
  7. W. Li, J. Han, and J. Pei, “CMAR: Accurate and efficient Classification Based on Multiple Class-Association Rules”, Proceedings of the ICDM, IEEE Computer Society, San Jose California, 2001, pp. 369-376.
  8. X. Yin, and J. Han, “CPAR: Classification based on Predictive Association Rules”, Proceedings of the SDM, SIAM, Francisco California, 2003.
  9. Xiao-Lin Li , Xiang-Dong He “A hybrid particle swarm optimization method for structure learning of probabilistic relational models” in transaction of Elsevier Information Sciences 283 (2014) 258–266
  10. Bahareh Bina, Oliver Schulte , Branden Crawford, Zhensong Qian, Yi Xiong “Simple decision forests for multi-relational classification ” in transaction of Elsevier Decision Support Systems 54 (2013) 1269–1279
  11. Geetha Manjunath , M. Narasimha Murty , Dinkar Sitaram “Combining heterogeneous classifiers for relational databases” in transaction of Elsevier Pattern Recognition 46 (2013) 317–324
  12. Tahar Mehenni , Abdelouahab Moussaoui “Data mining from multiple heterogeneous relational databases using decision tree classification” in transaction of Elsevier Pattern Recognition Letters 33 (2012) 1768–1775
  13. Marko Debeljaka , Aneta Trajanova, Daniela Stojanovaa, Florence Leprincec, Sa D zeroski “Using relational decision trees to model out-crossing rates in a multi-field setting” in Ecological Modelling 245 (2012) 75– 83
  14. Dewan Md. Farid , Li Zhang “Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks” in transaction of Elsevier of Expert Systems with Applications 41 (2014) 1937–1946
Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Classification Genetic Algorithm Multi Class Classification Dempster Shafer Theorem .