Weighted Voting Based Ensemble Classification with Hyper-parameter Optimization
Osman Gökalp, Erdal Taşçı · 2019 Innovations in Intelligent Systems and Applications Conference (ASYU) · 2019
Ensemble learning is one of the most popular research fields in machine learning and pattern recognition due to its contribution to the performance of a classification system. Voting based ensemble methods employs multiple learning algorithms and make the classification model more robust. Weighted voting based ensemble methods provide more flexible and fine-grained way to predict actual output classes compared to the unweighted (majority) voting based ensemble methods. However, the main drawback of weighted methods is to decide which values to be used. In this study, two hyper-parameter optimization strategies, namely Random Search and TPE, are used for optimizing weights of voting based ensemble classification. Experimental results based on seven machine learning datasets demonstrate the effectiveness of using hyper-parameter optimization for the purpose of finding optimal values for weighted voting based ensemble classification.