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Ordering-Based Kalman Filter Selective Ensemble for Classification
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DOI：10.1109/ACCESS.2020.2964849.png)
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摘要
En 中文
This paper investigates Kalman Filter-based Heuristic Ensemble (KFHE), which is a new perspective on multi-class ensemble classification with performance significantly better or at least as good as the state-of-the-art algorithms. We prove that the sample weight tuning method used in KFHE is a version of adaptive boosting, and the weight distribution does not change anymore and leads to redundant classifiers when the algorithm iterates enough times. This motivates us to select a sub-ensemble to alleviate the redundancy and improve the performance of the ensemble. An Ordering-based Kalman Filter Selective Ensemble (OKFSE) is proposed in this paper to select a sub-ensemble using the margin distance minimization approach. We demonstrate the effectiveness and robustness of OKFSE through extensive experiments on 20 real-world UCI datasets, and the statistical test shows that OKFSE significantly outperforms the state-of-the-art KFHE and clustering-based pruning methods on these datasets with 5% and 10% class label noise.
Keyword:
Machine learning
classification
selective ensemble
ordering-based pruning
Kalman filter
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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