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.


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


The Science and Information Organization

Date of publication




Persistent identifier


Document Type


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