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Dynamic ensemble algorithm post-selection using hardness-aware oracle


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Cordeiro, Paulo R. G., Cavalcanti, George D. C. and Cruz, Rafael M. O.. 2023. « Dynamic ensemble algorithm post-selection using hardness-aware oracle ». IEEE Access, vol. 11. pp. 86056-86070.

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Dynamic Ensemble Selection (DES) algorithms have obtained better performance in many tasks compared to monolithic classifiers and static ensembles. However, it is reasonable to assume that no DES algorithm is the optimal solution in different scenarios since diversity plays an important role. Thus, this paper addresses this research gap by proposing a novel approach called Hardness-aware Oracle with Dynamic Ensemble Selection (HaO-DES) that operates as a post-selection strategy, evaluating and selecting the best DES techniques per instance. Each DES technique ensemble is evaluated using a new measure called Hardness-aware Oracle (HaO). HaO extends the traditional Oracle concept by assessing a DES technique based on how the classifiers in the selected ensemble work together, contrasting with the individual classifier evaluation in the traditional assessment. We performed experiments over 30 databases, using three base classifiers (Perceptron, Logistic Regression, and Naive Bayes) in homogeneous and heterogenous pools’ configurations, to assess HaO-DES with four DES approaches (KNORA-U, KNOP, DES-P, and METADES). We use three performance metrics to evaluate the experiments: accuracy, F-score, and Matthews Correlation Coefficient (MCC). The results show that our approach outperforms or obtains similar results against the four individual DES approaches, mainly when considering heterogeneous pool settings.We also demonstrated the HaO-DES efficiency in choosing suitable DES techniques in different situations.

Item Type: Peer reviewed article published in a journal
Menelau Cruz, Rafael
Affiliation: Génie logiciel et des technologies de l'information
Date Deposited: 13 Sep 2023 17:41
Last Modified: 19 Oct 2023 15:58

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