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In this paper, a different hybrid approach based on combining Feature Selection, Fuzzy Weighted Pre-processing and Artificial Immune Recognition System is proposed to forecast the E.coli Promoter Gene Sequences, which has promoters in strings that represent nucleotides (one of A, G, T, or C). The proposed approach comprises three stages. In the first stage, the dimensionality of this dataset has been reduced to 4 attributes from 57 attributes by means of feature selection process by C4.5 decision tree rules. In the second stage, fuzzy weighted pre-processing has been used to weight E.coli Promoter Gene Sequences dataset that has 4 attributes in interval of [0,1]. Finally, AIRS classifier, is inspried from immune system, has been run to forecast the E.coli Promoter Gene Sequences. While only the AIRS algorithm obtained 53.85% prediction accuracy on the prediction of E.coli Promoter Gene Sequences using 50-50% training-test split, the proposed method obtained 90.38% prediction accuracy on the same conditions. This success shows that the proposed system is a robust and effective system in the prediction of E.coli Promoter Gene Sequences.

A robust approach for improved prediction of E.coli promoter gene sequences: combining feature selection, fuzzy weighted pre-processing and AIRS