Performance Evaluation of the Ensemble and Selected Machine Learning Techniques

  • Received
    November 25, 2022
  • Revised
    December 27, 2022
  • Accepted
    December 28, 2022
  • Published
    December 28, 2022


  • Nadia Majeed
  • Fawzia Ramo


Ensemble methods are machine-learning techniques that include the creation of several learners for a given task. Ensemble techniques aim to achieve high classification accuracy and improve performance. In predicting breast cancer, we require enhancing the accuracy of algorithms; therefore, we utilize here an ensemble technique that combines predictions of several models. In this study, the proposed ensemble hard voting classifier employs a combination of five machine learning algorithms: Support Vector Machine (SVM), K-Nearest Neighbours(K-NN), Naive Bayes (NB), Decision Tree (DT), and Random Forest (RF) is used to provide a binary classification for breast cancer. The results of the individual classifiers are then combined and compared with the performance of five individual classifiers with the hard voting classifier. The results show that ensemble-voting techniques perform better than single classifiers. The Wisconsin Breast Cancer Dataset (WBCD) from the UCI machine-learning repository was used in our experiments. The proposed ensemble hard voting classifier has given the highest accuracy value with 96.49%, whereas Support Vector Machine, Nearest Neighbours, Naive Bayes, Decision Tree, and Random Forest achieved accuracies of 95.32%, 92.39%, 94.73%, 92.98%, and 95.32% respectively on the breast cancer dataset.


Ensemble Learning, Hard Voting, machine learning, WBCD dataset (Wisconsin Breast Cancer Dataset)


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