Abstract
A hybrid unsupervised learning algorithm, which is termed as Evolutionary Rough Multi-Objective Optimization (ERMOO) algorithm, is proposed in this chapter. It comprises a judicious integration of the principles of the rough sets theory with the archived multi-objective simulated annealing approach. While the concept of boundary approximations of rough sets in this implementation deals with the incompleteness in the dynamic classification method with the quality of classification coefficient as the classificatory competence measurement, it enables faster convergence of the Pareto-archived evolution strategy. It incorporates both the rough set-based dynamic archive classification method in this algorithm. A measure of the amount of domination between two solutions is incorporated in this chapter to determine the acceptance probability of a new solution with an improvement in the spread of the non-dominated solutions in the Pareto-front by adopting rough sets theory. The performance is demonstrated on real-life breast cancer dataset for identification of Cancer Associated Fibroblasts (CAFs) within the tumor stroma, and the identified biomarkers are reported. Moreover, biological significance tests are carried out for the obtained markers.
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