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DOI: 10.14569/IJACSA.2018.090601
PDF

Generating Classification Rules from Training Samples

Author 1: Arun D. Kulkarni

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 6, 2018.

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Abstract: In this paper, we describe an algorithm to extract classification rules from training samples using fuzzy membership functions. The algorithm includes steps for generating classification rules, eliminating duplicate and conflicting rules, and ranking extracted rules. We have developed software to implement the algorithm using MATLAB scripts. As an illustration, we have used the algorithm to classify pixels in two multispectral images representing areas in New Orleans and Alaska. For each scene, we randomly selected 10 per cent of the samples from our training set data for generating an optimized rule set and used the remaining 90 per cent of samples to validate the extracted rules. To validate extracted rules, we built a fuzzy inference system (FIS) using the extracted rules as a rule base and classified samples from the training set data. The results in terms of confusion matrices are presented in the paper.

Keywords: Fuzzy membership functions; classification; rule extraction; multispectral images

Arun D. Kulkarni, “Generating Classification Rules from Training Samples” International Journal of Advanced Computer Science and Applications(IJACSA), 9(6), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090601

@article{Kulkarni2018,
title = {Generating Classification Rules from Training Samples},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090601},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090601},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {6},
author = {Arun D. Kulkarni}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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