A wrapper based binary bat algorithm with greedy crossover for attribute selection

Attribute selection plays a vital role in optimization and machine learning that involves huge datasets. Classification accuracy of any learning model depends on the dimensionality of data and attributes selected. This leads to a multi-objective problem of obtaining high classification accuracy with fewer attributes. In this research work, a multi-objective optimization algorithm with greedy crossover for attribute selection and classification is proposed. A wrapper based Binary Bat Algorithm (BBA) with Support Vector Machine (SVM) as evaluator is implemented for attribute selection. In general, the optimization algorithms have the tendency to prematurely converge with sub-optimal solutions. This reduces the quality of the attribute selected and efficiency of the algorithm. Here, a multi-objective binary bat algorithm with greedy crossover is proposed to reset the sub-optimal solutions that are obtained due to the premature convergence. The evaluation of the attributes selected is done using the Support Vector Machine with 10-fold cross-validation. The proposed algorithm is implemented and evaluated with the benchmark datasets available in the UC Irvine (UCI) repository. Classification accuracy of 89.25%, 96.45%, 96.57% and 88.50% using the Australian, Ionosphere, Wisconsin Breast Cancer (Original dataset) and Musk is obtained. Further analysis is made with parameter metrics like sensitivity, specificity, precision, recall, fmeasure, Matthews Correlation coefficient (MCC), confusion matrix and Area under the ROC Curve (AUC). The proposed multi-objective binary bat algorithm with greedy crossover yields better performance over the existing bat based algorithms and other nature-inspired algorithms. The solution for the multiobjective problem of obtaining high classification accuracy with minimal number of attributes is attained. Also, the problem of premature convergence occurring in the optimization algorithms with sub-optimal solutions is overcome using the proposed algorithm.

Automated summary

Attribute selection is crucial in optimization and machine learning, and a multi-objective binary bat algorithm with greedy crossover has been proposed for attribute selection and classification, achieving better performance than existing algorithms.