generating fuzzy rules for protein classification

this paper considers the generation of some interpretable fuzzy rules for assigning an amino acid sequence into the appropriate protein superfamily. since the main objective of this classifier is the interpretability of rules, we have used the distribution of amino acids in the sequences of proteins as features. these features are the occurrence probabilities of six exchange groups in the sequences. to generate the fuzzy rules, we have used some modified versions of a common approach. the generated rules are simple and understandable, especially for biologists. to evaluate our fuzzy classifiers, we have used four protein superfamilies from uniprot database. experimental results show the comprehensibility of generated fuzzy rules with comparable classification accuracy.