.png){kind=icon}
返回
Multivariate multi-layer classifier
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2022.108896.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Classification
Classifiers
Multivariate decision tree
Machine learning
Tree construction
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
Hearing Preservation in Patients with Vestibular Schwannomas with Sudden Sensorineural Hearing Loss
PREAI
分享收藏Induction of multiclass multifeature split decision trees from distributed data.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏Differentiable neural architecture learning for efficient neural networks用于高效神经网络的可微分神经体系结构学习PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏The use of the area under the roc curve in the evaluation of machine learning algorithmsroc曲线下面积在机器学习算法评价中的应用PATTERN RECOGNITION
OAAI
PATTERN RECOGNITIONIF7.6
OAAI分享收藏分享收藏Different mechanisms of magnetisation recorded in experimental fires: Archaeomagnetic implications
PREAI
分享收藏
查看更多内容