Generating balanced and strong clusters based on balance-constrained clustering approach (strong balance-constrained clustering) for improving ensemble classifier performance.

The application of clustering in generating random subspace has improved the accuracy and diversity of ensemble classification methods. If clusters are not balanced (unequal size of clusters) and not strong (unequal number of data from each class in each cluster), the results will deviate from classes with more samples in each cluster and thereby will be biased. The current paper presents a novel strong balance-constrained clustering or hard-strong clustering. This method creates diverse strong balanced data clusters to train different base classifiers and an artificial neural network with more than one hidden layer, in which the final decision is made for the data class through majority voting. By implementing the proposed method on 16 datasets, two objectives are followed: enhancing the performance of ensemble classifier and deep learning-based method (data mining objective), and adopting appropriate policies for budget, time, and energy assignment to various business domains by decision-makers (business objective). Based on the evaluation and comparison of the results, the proposed method is faster than other balancing methods. Furthermore, the accuracy of the proposed ensemble method has proved acceptable improvement than other ensemble classification methods. [ABSTRACT FROM AUTHOR]