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Multivariate multi-layer classifier
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DOI：10.1016/j.patcog.2022.108896.png)
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Abstract
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The variance-ratio binary multi-layer classifier (VRBMLC) has been recently proposed and shown to outperform conventional binary decision trees (BDTs). Though effective with better interpretability, the VRBMLC generates deep layers of tree nodes as it employs a one-feature-at-a-time binary split at each layer. To further condense the tree depth and enhance the classification performance, this research pro-poses a multivariate multi-layer classifier that applies a variance-ratio criterion to enable ternary splits of each tree node and that integrates the oblique discriminant hyperplane in the tree node. We benchmark 16 state-of-the-art univariate and multivariate classifiers on 43 publicly available datasets. The results show that the proposed methods greatly simplify the tree structure and yield a significantly higher aver-age accuracy.(c) 2022 Elsevier Ltd. All rights reserved.
Keywords:
Classification
Classifiers
Multivariate decision tree
Machine learning
Tree construction
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W
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