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Combining Linear Classifiers Using Probability-Based Potential Functions
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DOI：10.1109/ACCESS.2020.3038341.png)
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摘要
En 中文
The score function can be used as a measure for evaluating predicted probabilities of the classification models. In multiple classifiers systems, one of the problems is the diversity of the way of determining the scoring function of individual base classifiers. To alleviate this limitation, in this article, we propose a novel concept of calculating a scoring function defined by the probability-based potential function. The proposed potential functions take into account the distance of the recognized object from the decision boundary as well as a prior probability of the class labels. The proposed score function has the same nature for all linear base classifiers, which defined the multiple classifiers model. Additionally, the proposed method is compared with other ensemble algorithms based on homogeneous linear base classifiers. The experiments on seventy databases demonstrate the effectiveness of our method. To discuss the results of our experiments, we use multiple classification performance measures dedicated to standard and imbalanced datasets. The statistical analysis of the experiments is also performed.
Keyword:
Kernel
Training
Estimation
Task analysis
Random variables
Supervised learning
Standards
Ensemble of classifiers
linear classifier
score function
supervised learning
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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