An optimal pruning algorithm of classifier ensembles: dynamic programming approach
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S.I. : APPLYING ARTIFICIAL INTELLIGENCE TO THE INTERNET OF THINGS
An optimal pruning algorithm of classifier ensembles: dynamic programming approach Omar A. Alzubi1 • Jafar A. Alzubi2 Manikandan Ramachandran3
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Mohammed Alweshah1 • Issa Qiqieh2 • Sara Al-Shami1
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Received: 22 September 2019 / Accepted: 24 January 2020 Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract In recent years, classifier ensemble techniques have drawn the attention of many researchers in the machine learning research community. The ultimate goal of these researches is to improve the accuracy of the ensemble compared to the individual classifiers. In this paper, a novel algorithm for building ensembles called dynamic programming-based ensemble design algorithm (DPED) is introduced and studied in detail. The underlying theory behind DPED is based on cooperative game theory in the first phase and applying a dynamic programming approach in the second phase. The main objective of DPED is to reduce the size of the ensemble while encouraging extra diversity in order to improve the accuracy. The performance of the DPED algorithm is compared empirically with the classical ensemble model and with a well-known algorithm called ‘‘the most diverse.’’ The experiments were carried out with 13 datasets from UCI and three ensemble models. Each ensemble model is constructed from 15 different base classifiers. The experimental results demonstrate that DPED outperforms the classical ensembles on all datasets in terms of both accuracy and size of the ensemble. Regarding the comparison with the most diverse algorithm, the number of selected classifiers by DPED across all datasets and all domains is less than or equal to the number selected by the most diverse algorithm. Experiment on blog spam dataset, for instance, shows that DPED provides an accuracy of 96.47 compared to 93.87 obtained by the most diverse using 40% training size. Finally, the experimental results verify the reliability, stability, and effectiveness of the proposed DPED algorithm. Keywords Machine learning Classification Classifier ensembles Dynamic programming Diversity Game theory Cooperative game & Jafar A. Alzubi [email protected] Omar A. Alzubi [email protected] Mohammed Alweshah [email protected] Issa Qiqieh [email protected] Sara Al-Shami [email protected] Manikandan Ramachandran [email protected] 1
Prince Abdullah bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, Jordan
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Faculty of Engineering, Al-Balqa Applied University, Al-Salt, Jordan
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School of Computing, SASTRA Deemed University, Thanjavur, India
1 Introduction Empirically, classifier ensembles have been used in many applications because they are more stable and, more importantly, predict better than single classifiers. In the medical field, Abdar et al. [1] proposed a hybrid ensemble model that uses different K-fold cross-validation (K-CV) techniques to predict breast c
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