A NOVEL APPROACH TO CLASSIFICATION OF GENE EXPRESSION DATA USINGMULTI OBJECTIVE EVOLUTIONARY
Keywords:
Pareto optimality, Genetic, Multi-objective Optimization, Evolutionary algorithms, Classification, Algorithm.Abstract
Multiobjective evolutionary algorithms are evolutionary systems which are used for optimizing various measures of the evolving systems. Most of the real life data mining problems are optimization problems, where the aim is to evolve a candidate model that optimizes certain performance criteria. Classification problem can be thought of as multi-objective problem as itmay require to optimize accuracy, model complexity, interestingness, misclas-sification rate, sensitivity, specificity etc. The performance of these MOEAs used is depends on various characteristics like evolutionary techniques used, chromosome representation, parameters like population size, crossover rate, mutation rate, stopping criteria, number of generations, objectives taken for optimization, fitness function used, optimization strategy etc. This paper reports the comprehensive survey on recent developments in the multi-objective evolutionary algorithms for classification problems.
