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DDES: A Distribution-Based Dynamic Ensemble Selection Framework
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DOI：10.1109/ACCESS.2021.3063254.png)
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Abstract
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Dynamic Ensemble Selection (DES) is a special type of ensemble modeling that selects different subsets of base classifiers for different test sample cases. In this process, multiple base classifiers are compared in terms of their competence as an ensemble to the best possible prediction for a given test sample case. Traditional DES methods rely on the Euclidean distance-based k-Nearest Neighbor (kNN) algorithm to identify the most relevant reference data points in such a way that the base classifiers correctly classifying them can be considered for an optimal ensemble. However, this approach is sensitive to the local structure of the data and the presence of noisy or irrelevant attributes. This study proposes a novel distribution-based DES (DDES) framework that takes the data structure into consideration when selecting reference data points. The experimental results for 30 classification problems indicate that the proposed approach yielded the best accuracy among the competing DES methods. Additionally, we discuss the correlation between the data complexity and the improvement in classification performance.
Keywords:
Classification algorithms
Diversity reception
Prediction algorithms
Noise measurement
Licenses
Indexes
Heuristic algorithms
Multiple classifier systems
dynamic ensemble selection
region of competence
feature variance
data complexity
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