基于标记相关性的多标记三支分类算法

doi:10.6040/j.issn.1671-9352.1.2019.162

摘要/Abstract

摘要：

提出了一种基于标记相关性的多标记三支分类算法TML_LC,该算法利用三支决策模型将多标记样本空间划分为接受域、拒绝域和边界域,然后利用概率图模型构建标记之间的相关性,并应用于边界域的延迟决策,从而降低分类模型的时间复杂度,并提高分类模型的精度。

关键词: 多标记学习, 三支决策, 标记相关性, 延迟决策

Abstract:

This paper uses the probability map model to the tag relationship is encoded, and three-way three decision models are used to solve the uncertainty of the data samples. A multi-label classification algorithm based on three-way decision-correlation correlation is proposed. The algorithm will solve the two-way decision problem in multi-label classification(TML_LC). The SVM mapping is divided into accepted domain, rejected domain and uncertain domain. The probability map model is used to consider the correlation between labels to transform the uncertainty of the uncertain domain, so as to improve the accuracy of the classification model.

Key words: multi-label learning, three-way decision, label correlation, delayed decision

中图分类号:

TP391

余鹰,吴新念,王乐为,张应龙. 基于标记相关性的多标记三支分类算法[J]. 《山东大学学报(理学版)》, 2020, 55(3): 81-88.

Ying YU,Xin-nian WU,Le-wei WANG,Ying-long ZHANG. A multi-label three-way classification algorithm based on label correlation[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2020, 55(3): 81-88.

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	l₁	l₂	l₃	l₄
l₁	0	1	0	1
l₂	1	0	1	0
l₃	0	0	0	0
l₄	0	0	1	0

数据集名称	样本个数	特征个数	标记个数	平均标记数	标记密度
yeast	2 417	103	14	4.237	0.303
scene	2 407	294	6	1.074	0.179
emotions	593	72	6	1.869	0.311

数据集	Algorithm
数据集	TML_LC	BSVM	ML-KNN	BPMLL	ECC
yeast	0.198±0.013	0.199±0.010	0.195±0.011	0.205±0.010	0.208±0.010
emotions	0.195±0.011	0.199±0.022	0.194±0.013	0.219±0.021	0.192±0.021
scene	0.101±0.006	0.104±0.006	0.084±0.008	0.282±0.014	0.096±0.010

数据集	Algorithm
数据集	TML_LC	BSVM	ML-KNN	BPMLL	ECC
yeast	0.222±0.019	0.230±0.023	0.228±0.029	0.235±0.030	0.176±0.022
emotions	0.244±0.049	0.253±0.070	0.263±0.067	0.318±0.057	0.216±0.085
scene	0.179±0.080	0.250±0.027	0.219±0.029	0.821±0.031	0.226±0.034

数据集	Algorithm
数据集	TML_LC	BSVM	ML-KNN	BPMLL	ECC
yeast	0.458±0.022	0.514±0.018	0.447±0.014	0.456±0.019	0.516±0.015
emotions	0.284±0.033	0.295±0.027	0.300±0.019	0.300±0.022	0.322±0.022
scene	0.086±0.010	0.089±0.009	0.078±0.010	0.374±0.024	0.091±0.008