JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2020, Vol. 55 ›› Issue (3): 81-88.doi: 10.6040/j.issn.1671-9352.1.2019.162

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A multi-label three-way classification algorithm based on label correlation

Ying YU*,Xin-nian WU(),Le-wei WANG,Ying-long ZHANG   

  1. College of Software, East China Jiaotong University, Nanchang 330013, Jiangxi, China
  • Received:2019-05-21 Online:2020-03-20 Published:2020-03-27
  • Contact: Ying YU E-mail:yuyingjx@163.com

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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

CLC Number: 

  • TP391

Fig.1

Multi-label image"

Fig.2

SVM classification with boundary threshold"

Fig.3

Three decisions based on single evaluation function"

Table 1

A example of label correlation matri"

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

Table 2

Description of the experimental data set"

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

Fig.4

Average precision varies with βand αon yeast"

Fig.5

Average precision varies with αand βon scene"

Fig.6

Average Precision varies with βand α on emotions"

Table 3

Hamming loss of five multi-label algorithms on three datasets(mean±std)"

数据集 Algorithm
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

Table 4

One-error of five multi-label algorithms on three datasets(mean±std)"

数据集 Algorithm
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

Table 5

Coverage of five multi-label algorithms on three datasets(mean±std)"

数据集 Algorithm
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

Table 6

Ranking loss of five multi-label algorithms on three datasets(mean±std)"

数据集 Algorithm
TML_LC BSVM ML-KNN BPMLL ECC
yeast 0.171±0.014 0.200±0.013 0.166±0.015 0.171±0.015 0.285±0.022
emotions 0.147±0.030 0.156±0.034 0.163±0.022 0.173±0.020 0.233±0.040
scene 0.086±0.012 0.089±0.011 0.076±0.012 0.434±0.026 0.135±0.013

Table 7

Average precision of five multi-label algorithms on three datasets(mean±std)"

数据集 Algorithm
TML_LC BSVM ML-KNN BPMLL ECC
yeast 0.761±0.016 0.794±0.019 0.765±0.021 0.754±0.020 0.728±0.019
emotions 0.817±0.031 0.807±0.037 0.799±0.031 0.779±0.027 0.796±0.042
scene 0.871±0.017 0.849±0.016 0.869±0.017 0.445±0.018 0.852±0.016
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