Today’s datasets are usually very large with many features and making analysis on such datasets is really a tedious task. Especially when performing classification, selecting attributes that are salient for the process is a brainstorming task. It is more difficult when there are many class labels for the target class attribute and hence many researchers have introduced methods to select features for performing classification on multi-class attributes. The process becomes more tedious when the attribute values are imbalanced for which researchers have contributed many methods. But, there is no sufficient research to handle extreme imbalance and feature selection together and hence this paper aims to bridge this gap. Here Particle Swarm Optimization (PSO), an efficient evolutionary algorithm is used to handle imbalanced dataset and feature selection process is also enhanced with the required functionalities. First, Multi-objective Particle Swarm Optimization is used to transform the imbalanced datasets into balanced one and then another version of Multi-objective Particle Swarm Optimization is used to select the significant features. The proposed methodology is applied on eight multi-class extremely imbalanced datasets and the experimental results are found to be better than other existing methods in terms of classification accuracy, G mean, F measure. The results validated by using Friedman test also confirm that the proposed methodology effectively balances the dataset with less number of features than other methods.

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Genetic algorithm is an intelligent way for solving combinatorial, NP hard problems and many other problems which cannot be easily solved by applying traditional mathematical formula. The proposed method gives a new variant of the Standard Genetic algorithm which is very simple and will easily find the solution even for complex problems. It implements the concept of grouping to reach the optimal solution. The selection pressure which is a crucial factor that determines the efficiency of the algorithm is very much reduced in the proposed algorithm. The convergence velocity of the algorithm is greatly improved thereby reducing the time taken for the algorithm to reach the solution.

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