Classification of Breast Cancer using User-Defined Weighted Ensemble Voting Scheme

Abstract

When weak classifiers, i.e., estimators, are not justifying the classification of breast cancer then ensemble learning is a way to improve the classification of cancer. The ensemble is basically an aggregator where all weak classifiers are merged to get a strong classifier. The ensemble is based on a majority voting scheme. A hard voting scheme is used to take a major vote of each classifier whereas a soft voting scheme takes the weights of the probability of each classifier. A custom based weights are assigned in this paper and the final classification of cancer using ensemble classifier is outperformed than each estimator. The highest accuracy from the proposed ensemble classifier is achieved up to 96.47% where the lowest estimator got 93.18 %. The AUC score of ensemble classifier achieved is 0.9948 which is one of the highest among all other estimators.