A robust approach for improved prediction of E.coli promoter gene sequences: combining feature selection, fuzzy weighted pre-processing and AIRS | ||||
| The International Conference on Electrical Engineering | ||||
| Article 14, Volume 6, 6th International Conference on Electrical Engineering ICEENG 2008, May 2008, Page 1-13 PDF (113.15 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/iceeng.2008.34197 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Kemal Polat; Salih Güne | ||||
| Selcuk University, Electrical and Electronics Engineering Department, 42035, Konya, TURKEY. | ||||
| Abstract | ||||
| Abstract: 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. | ||||
| Keywords | ||||
| E.coli Promoter Gene Sequences; prediction; AIRS; Feature Selection; Fuzzy Weighted Pre-processing; Hybrid System | ||||
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