Abstract
Machine learning has become a powerful approach in practical applications such as decision making , sentiment analysis and ontology engineering. In order to improve the overall performance in machine learning tasks, ensemble learning has become increasingly popular by combining different learning algorithms or models. Popular approaches of ensemble learning include Bagging and Boosting, which involve voting towards the final classification. The voting in both Bagging and Boosting could result in incorrect classification due to the bias in the way voting takes place. In order to reduce the bias in voting, this paper proposes a probabilistic approach of voting in the context of granular computing towards improvement of overall accuracy of classification. An experimental study is reported to validate the proposed approach of voting by using 15 data sets from the UCI repository. The results show that probabilistic voting is effective in increasing the accuracy through reduction of the bias in voting. This paper contributes to the theoretical and empirical analysis of causes of bias in voting, towards advancing ensemble learning approaches through the use of probabilistic voting.
Original language | English |
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Pages (from-to) | 131–139 |
Number of pages | 9 |
Journal | Granular Computing |
Volume | 2 |
Issue number | 3 |
Early online date | 11 Nov 2016 |
DOIs | |
Publication status | Published - 1 Sept 2017 |
Keywords
- Granular Computing
- Machine Learning
- Ensemble Learning
- Bagging
- Boosting
- Probabilistic Voting