Hybrid Ant Bee Algorithm for Fuzzy Expert System Based Sample Classification

• Published in: IEEE/ACM transactions on computational biology and bioinformatics Vol. 11; no. 2; pp. 347 - 360
• Main Authors: GaneshKumar, Pugalendhi, Rani, Chellasamy, Devaraj, Durairaj, Victoire, T. Aruldoss Albert
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; ant colony optimization; artificial bee colony; Computational biology; Computational Biology - methods; Data models; Databases, Factual; Diabetes Mellitus, Type 2 - genetics; Diabetes Mellitus, Type 2 - metabolism; Expert systems; fuzzy expert system; Fuzzy Logic; Fuzzy systems; Gene expression; Gene Expression Profiling - methods; Humans; Microarray data; Models, Biological; mutual information; Neoplasms - genetics; Neoplasms - metabolism; Oligonucleotide Array Sequence Analysis; ROC Curve
• ISSN: 1545-5963; 1557-9964
• DOI: 10.1109/TCBB.2014.2307325

Accuracy maximization and complexity minimization are the two main goals of a fuzzy expert system based microarray data classification. Our previous Genetic Swarm Algorithm (GSA) approach has improved the classification accuracy of the fuzzy expert system at the cost of their interpretability. The if-then rules produced by the GSA are lengthy and complex which is difficult for the physician to understand. To address this interpretability-accuracy tradeoff, the rule set is represented using integer numbers and the task of rule generation is treated as a combinatorial optimization task. Ant colony optimization (ACO) with local and global pheromone updations are applied to find out the fuzzy partition based on the gene expression values for generating simpler rule set. In order to address the formless and continuous expression values of a gene, this paper employs artificial bee colony (ABC) algorithm to evolve the points of membership function. Mutual Information is used for idenfication of informative genes. The performance of the proposed hybrid Ant Bee Algorithm (ABA) is evaluated using six gene expression data sets. From the simulation study, it is found that the proposed approach generated an accurate fuzzy system with highly interpretable and compact rules for all the data sets when compared with other approaches.