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A multi-view OVA model based on decision tree for multi-classification tasks

机译：基于决策树的多视图OVA模型

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摘要

Decision tree is a simple classification algorithm and has been widely used in knowledge discovery and pattern recognition fields, which can be used to deal with the multi-classification tasks. In this paper, we present a multi-view OVA model based on decision tree (MVDT) for multi-classification tasks to simplify the structure of the decision tree and improve the generalization ability. A multi-class classification task is divided into c multiple parallel sub-tasks, and MVDT builds c decision trees as base binary classifiers for each sub-task. Each decision tree gives membership vector for each leaf node to estimate the probabilities of the instances in the leaf node belonging to negative classes, as well as presents a precise classification for positive class. Thus, one can obtain more information about instances belonging to negative classes through membership vectors, which helps to achieve higher accuracy and better robustness for classification. As a general framework, MVDT algorithm can use any existing decision tree model as base classifier. To evaluate the performance of our algorithm, we choose C4.5, CART, TEIM, SCDT and NBTree as base classifiers in MVDT. The experiments on 22 data sets show that the proposed MVDT has excellent performance for multi-class classification problems and has excellent robustness to output noise. (C) 2017 Elsevier B.V. All rights reserved.
机译：决策树是一种简单的分类算法，已广泛应用于知识发现和模式识别领域，可用于处理多分类任务。在本文中，我们提出了一种基于决策树（MVDT）的多视图OVA模型，用于多分类任务，以简化决策树的结构并提高泛化能力。多类分类任务被划分为c个多个并行子任务，而MVDT为每个子任务构建c个决策树作为基础二进制分类器。每个决策树为每个叶节点提供成员向量，以估计叶节点中属于负类的实例的概率，并为正类提供精确的分类。因此，人们可以通过隶属度矢量获得更多关于属于否定类的实例的信息，这有助于实现更高的准确性和更好的分类鲁棒性。作为通用框架，MVDT算法可以使用任何现有的决策树模型作为基础分类器。为了评估算法的性能，我们选择C4.5，CART，TEIM，SCDT和NBTree作为MVDT中的基础分类器。在22个数据集上进行的实验表明，所提出的MVDT在处理多类分类问题方面具有出色的性能，并且对输出噪声具有出色的鲁棒性。 （C）2017 Elsevier B.V.保留所有权利。

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  • 来源
    《Knowledge-Based Systems》 |2017年第15期|208-219|共12页
  • 作者

    Guan Xiaoqiang; Liang Jiye; Qian Yuhua; Pang Jifang;

  • 作者单位

    Shanxi Univ, Sch Comp & Informat Technol, Minist Educ, Key Lab Computat Intelligence & Chinese Informat, Taiyuan 030006, Shanxi, Peoples R China;

    Shanxi Univ, Sch Comp & Informat Technol, Minist Educ, Key Lab Computat Intelligence & Chinese Informat, Taiyuan 030006, Shanxi, Peoples R China;

    Shanxi Univ, Sch Comp & Informat Technol, Minist Educ, Key Lab Computat Intelligence & Chinese Informat, Taiyuan 030006, Shanxi, Peoples R China;

    Shanxi Univ, Sch Comp & Informat Technol, Minist Educ, Key Lab Computat Intelligence & Chinese Informat, Taiyuan 030006, Shanxi, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Decision tree; Multi-class classification; OVA; Membership vector;

    机译：决策树;多类分类;OVA;成员向量;

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