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