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A Fuzzy Classifier based on Product and Sum Aggregation Reasoning Rule

by U. V. Kulkarni, S. V. Shinde
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 62 - Number 5
Year of Publication: 2013
Authors: U. V. Kulkarni, S. V. Shinde
10.5120/10075-4687

U. V. Kulkarni, S. V. Shinde . A Fuzzy Classifier based on Product and Sum Aggregation Reasoning Rule. International Journal of Computer Applications. 62, 5 ( January 2013), 9-14. DOI=10.5120/10075-4687

@article{ 10.5120/10075-4687,
author = { U. V. Kulkarni, S. V. Shinde },
title = { A Fuzzy Classifier based on Product and Sum Aggregation Reasoning Rule },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 62 },
number = { 5 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 9-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume62/number5/10075-4687/ },
doi = { 10.5120/10075-4687 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:10:53.100465+05:30
%A U. V. Kulkarni
%A S. V. Shinde
%T A Fuzzy Classifier based on Product and Sum Aggregation Reasoning Rule
%J International Journal of Computer Applications
%@ 0975-8887
%V 62
%N 5
%P 9-14
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper proposes the algorithm ProSum to perform the supervised classification of the data. In the proposed algorithm data is fuzzified by using ?–type membership function to give the feature belongingness of each pattern to each class. By using Product aggregation reasoning rule (PARR) and sum aggregation reasoning rule (SARR), the belongingness of each pattern to each class is determined. Finally by using defuzzification operation each pattern is assigned with the predicted class label. In this paper, proposed algorithm is applied to four dataset: IRIS, WINE, BUPA and PIMA. Accuracy of the results is measured by using the performance measures Misclassification (MC), Percentage of overall class Accuracy (PA) and Kappa Index of Agreement (KIA). The performance of ProSum is compared with C4. 5 and PARR.

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Index Terms

Computer Science
Information Sciences

Keywords

Classification Fuzzy logic Aggregation operator ?-type membership function

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