Fusion of stability and multi-objective optimization for solving cancer tissue classification problem. (15th December 2018)

• Record Type: Journal Article
• Authors: Mitra, Sayantan; Saha, Sriparna; Acharya, Sudipta
• Is Part Of: Expert systems with applications. Volume 113(2018)
• Page Start: 377
• Page End: 396
• Publication Date: 2018-12-15
• Subjects: Multi-objective optimization -- Stability -- Simulated annealing -- Cancer-tissue sample classification; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2018.06.059
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A multiobjective clustering technique using the concepts of stability is proposed. The hypothesis is that small perturbations cannot destroy the optimal structure. Proposed algorithm is not depended on the number of perturbed datasets used. Results are shown for cancer tissue sample classification. Biological and statistical significance tests are also conducted. Abstract: The concept of stability is one of the commonly used physical phenomena. Current paper builds on the hypothesis that the optimal number of clusters present in the dataset corresponds to that partitioning which is most stable over some small changes in the dataset. In order to quantify the degree of stability, a new measure is also proposed in the paper. Thereafter an expert clustering approach is developed in the current paper which utilizes the properties of stability for automatically detecting the number of clusters from a given dataset. Initially, several different variants of the dataset are generated by introducing small perturbations. A multi-objective based expert clustering framework is developed to automatically partition different variants of the data. A new objective function, capturing stability property of clustering solution namely ' Agreement -index', along with two well-known objective functions are optimized simultaneously using a multi-objective simulated annealing based process, namely AMOSA for the purpose of clustering. Finally, the problem of cancer classification is addressed as the application domain of the proposed expert framework. Results of our newly developed stability based clustering namely Stab-clustering with respect to existing approaches are shown for twelve microarray cancer datasets in terms of different cluster quality measures. The obtained results confirm the robustness of our proposed technique over state-of-the-art. A thorough biological and statistical significance tests are also conducted to prove the effectiveness of the proposed approach.