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Fuzzy ARTMAP with Relevance factor (FAMR) is a Fuzzy ARTMAP (FAM) neural architecture with the following property: Each training pair has a relevance factor assigned to it, proportional to the importance of that pair during the learning phase. Using a relevance factor adds more flexibility to the training phase, allowing ranking of sample pairs according to the confidence we have in the information source or in the pattern itself.

We introduce a novel FAMR architecture: FAMR with Feature Weighting (FAMRFW). In the first stage, the training data features are weighted. In our experiments, we use a feature weighting method based on Onicescu’s informational energy (IE). In the second stage, the obtained weights are used to improve FAMRFW training. The effect of this approach is that category dimensions in the direction of relevant features are decreased, whereas category dimensions in the direction of non-relevant feature are increased. Experimental results, performed on several benchmarks, show that feature weighting can improve the classification performance of the general FAMR algorithm.


This article was originally published Open Access in International Journal of Computers, Communications & Control. The full-text article from the publisher can be found here.


​International Journal of Computers, Communications & Control

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Creative Commons Attribution-NonCommercial 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License


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