AI Article Synopsis
- The imbalance problem in datasets makes it tough for classifiers to learn proper decision boundaries, leading to poor classification performance.
- Various ensemble algorithms have been created to tackle imbalance issues, but they often ignore the potential of effective feature space exploration and treat all base classifiers equally without recognizing their different impacts.
- Our proposed algorithm combines advanced data transformation with adaptive weighted voting, using modified metric learning for better feature space and assigning varying weights to base classifiers, showing promising results in experiments with imbalanced datasets.
Article Abstract
The imbalance problem is widespread in real-world applications. When training a classifier on the imbalance datasets, the classifier is hard to learn an appropriate decision boundary, which causes unsatisfying classification performance. To deal with the imbalance problem, various ensemble algorithms are proposed. However, conventional ensemble algorithms do not consider exploring an effective feature space to further improve the performance. In addition, they treat the base classifiers equally and ignore the different contributions of each base classifier to the ensemble result. In order to address these problems, we propose a novel ensemble algorithm that combines effective data transformation and an adaptive weighted voting scheme. First, we utilize modified metric learning to obtain an effective feature space based on imbalanced data. Next, the base classifiers are assigned different weights adaptively. The experiments on multiple imbalanced datasets, including images and biomedical datasets verify the superiority of our proposed ensemble algorithm.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8918481 | PMC |
| http://dx.doi.org/10.3389/fnbot.2022.827913 | DOI Listing |
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