A Weighted Voting Classifier Based on Differential Evolution
TL;DR: Experimental results show that the proposed weighted voting approach based on differential evolution not only improves the classification accuracy, but also has a strong generalization ability and universality.
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Abstract: Ensemble learning is to employ multiple individual classifiers and combine their predictions, which could achieve better performance than a single classifier. Considering that different base classifier gives different contribution to the final classification result, this paper assigns greater weights to the classifiers with better performance and proposes a weighted voting approach based on differential evolution. After optimizing the weights of the base classifiers by differential evolution, the proposed method combines the results of each classifier according to the weighted voting combination rule. Experimental results show that the proposed method not only improves the classification accuracy, but also has a strong generalization ability and universality.
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Citations
A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification
TL;DR: Experimental analysis of classification tasks, including sentiment analysis, software defect prediction, credit risk modeling, spam filtering, and semantic mapping, suggests that the proposed ensemble method can predict better than conventional ensemble learning methods such as AdaBoost, bagging, random subspace, and majority voting.
369
Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project
Manal Alghamdi,Mouaz H. Al-Mallah,Mouaz H. Al-Mallah,Steven J. Keteyian,Clinton A. Brawner,Jonathan K. Ehrman,Sherif Sakr +6 more
TL;DR: The study shows the potential of ensembling and SMOTE approaches for predicting incident diabetes using cardiorespiratory fitness data and applies different techniques to uncover potential predictors of diabetes.
A Weighted Majority Voting Ensemble Approach for Classification
Alican Dogan,Derya Birant +1 more
- 01 Sep 2019
TL;DR: A novel Weighted Majority Voting Ensemble approach is proposed, which evaluates individual performances of each classifier in the ensemble and adjusts their contributions to class decision and the effectiveness of the proposed method is confirmed by the experimental results.
113
Emotion Recognition by Textual Tweets Classification Using Voting Classifier (LR-SGD)
Anam Yousaf,Muhammad Umer,Saima Sadiq,Saleem Ullah,Seyedali Mirjalili,Vaibhav Rupapara,Michele Nappi +6 more
TL;DR: In this article, seven Machine Learning models are implemented for emotion recognition by classifying tweets as happy or unhappy. And the proposed voting classifier(LR-SGD) with TF-IDF produces the most optimal result with 79% accuracy and 81% F1 score.
Evaluating the effect of voting methods on ensemble-based classification
Florin Leon,Sabina-Adriana Floria,Costin Badica +2 more
- 03 Jul 2017
TL;DR: The results reveal that the single transferable vote can be a good alternative to plurality voting, although it has the drawback of a higher computational cost related to the calculation of preference ordering.
83
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