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.
Description
This article was originally published in the Internation Journal of Advanced Computer Science and Applications (IJACSA), under a Creative Commons Attribution 4.0 International License. DOI: http://dx.doi.org/10.14569/IJACSA.2018.090601
Publisher
The Science and Information Organization
Date of publication
2018
Language
english
Persistent identifier
http://hdl.handle.net/10950/1175
Document Type
Article
Recommended Citation
Kulkarni, Arun D., "Generating Classification Rules from Training Samples" (2018). Computer Science Faculty Publications and Presentations. Paper 19.
http://hdl.handle.net/10950/1175
Publisher Citation
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