An Adaptive and Diversity-Based Ensemble Method for Binary Classification

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  • In recent years, machine learning techniques have been rapidly developed and widely applied to many industrial and academic fields. Moreover, as an important part of machine learning, ensemble techniques have drawn significant attention in both academic researches and practical applications, which make use of multiple single models to construct a hybrid model. Usually, compared to each individual model, a better performance can be achieved by ensemble methods. In this thesis, a novel ensemble method is proposed to improve the performance for binary classification. The proposed method can non-linearly combine the base models by adaptively selecting the most suitable one for each data instance. The new approach has been validated on two datasets, and the experiments results show an up to 18.5% improvement on F1 score compared to the best individual model. In addition, the proposed method outperforms two other commonly used ensemble methods (Averaging and Stacking) in improving F1 score.

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  • Copyright © 2017 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
Date Created
  • 2017


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