一种适应于非完备标签数据和标签关联性的多标签分类方法

doi:10.11959/j.issn.1000-0801.2016197

Abstract

Abstract:

Multi-label classification methods have been applied in many real-world fields，in which the labels may have strong relevance and some of them even are incomplete or missing.However，existing multi-label classification algorithms are unable to handle both issues simultaneously.A new probabilistic model that can automatically learn and exploit multi-label relevance was proposed on label relevance and missing label classification simultaneously.By integrating out the missing information，it also provides a disciplined approach to handle missing labels.Experiments on a number of real world data sets with both complete and incomplete labels demonstrated that the proposed method can achieve higher classification and prediction evaluation scores than the existing multi-label classification algorithms.

Key words: incomplete label, label relevance, multi-label classification, probabilistic model

Lina ZHANG,Lingpeng DAI,Tai KUANG. A multi-label classification method for disposing incomplete labeled data and label relevance[J]. Telecommunications Science, 2016, 32(8): 82-89.

Figures/Tables 4

参数	数据集	ILDLR	MLRGL	FastTag	BR
macro_F1（k=1）	MIRFLICKR	0.467 5±0.001 4	0.458 1±0.008 9	0.461 1±0.010 2	0.455 6±0.002 1
	COREL5K	0.240 7±0.054 1	0.110 8±0.015 1	0.199 7±0.010 1	0.142 2±0.027 9
	ESPGAME	0.233 1±0.019 1	0.147 1±0.011 1	0.179 1±0.007 8	0.193 9±0.014 1
	IAPRTCL12	0.237 1±0.045 3	0.058 9±0.038 9	0.192 4±0.004 9	0.209 6±0.033 4
micro_F1（k=1）	MIRFLICKR	0.467 8±0.002 1	0.457 8±0.006 7	0.462 3±0.010 3	0.453 4±0.001 1
	COREL5K	0.240 0±0.053 9	0.111 1±0.013 4	0.198 7±0.011 1	0.141 9±0.027 8
	ESPGAME	0.233 1±0.019 2	0.144 8±0.010 1	0.187 9±0.007 8	0.193 2±0.014 3
	IAPRTCL12	0.236 1±0.045 3	0.056 7±0.040 1	0.193 1±0.004 2	0.209 6±0.034 4
macro_F1（k=2）	MIRFLICKR	0.453 2±0.006 5	0.456 7±0.007 8	0.434 4±0.051 0	0.443 4±0.002 6
	COREL5K	0.240 5±0.050 9	0.110 5±0.015 1	0.181 2±0.011 9	0.016 7±0.011 1
	ESPGAME	0.231 1±0.020 2	0.145 5±0.011 4	0.221 6±0.004 9	0.183 4±0.012 6
	IAPRTCL12	0.234 4±0.043 7	0.058 8±0.038 7	0.181 1±0.006 5	0.197 1±0.033 1
micro_F1（k=2）	MIRFLICKR	0.435 4±0.005 6	0.439 8±0.006 5	0.415 4±0.044 5	0.422 1±0.004 6
	COREL5K	0.239 7±0.053 5	0.111 1±0.014 3	0.181 1±0.005 7	0.015 6±0.009 1
	ESPGAME	0.223 1±0.024 5	0.144 5±0.012 1	0.216 7±0.008 9	0.174 4±0.013 9
	IAPRTCL12	0.231 1±0.035 4	0.058 7±0.037 7	0.185 7±0.008 4	0.193 1±0.032 1
macro_F1（k=3）	MIRFLICKR	0.460 1±0.002 3	0.456 7±0.008 7	0.454 3±0.017 8	0.412 1±0.001 1
	COREL5K	0.235 6±0.053 1	0.112 1±0.014 3	0.180 1±0.004 5	0.025 6±0.015 4
	ESPGAME	0.223 4±0.023 2	0.145 7±0.011 1	0.214 5±0.006 7	0.160 1±0.010 1
	IAPRTCL12	0.231 1±0.033 4	0.058 8±0.037 8	0.183 2±0.004 3	0.164 5±0.023 4
micro_F1（k=3）	MIRFLICKR	0.444 4±0.003 1	0.435 6±0.004 3	0.415 1±0.016 1	0.386 7±0.002 4
	COREL5K	0.236 7±0.052 1	0.107 6±0.015 4	0.173 2±0.005 6	0.024 5±0.016 1
	ESPGAME	0.212 3±0.025 5	0.142 1±0.011 2	0.203 4±0.005 6	0.156 7±0.012 3
	IAPRTCL12	0.224 5±0.037 8	0.056 7±0.038 1	0.174 5±0.004 8	0.164 3±0.022 1

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数据集	标签数/个	样点数/个	每个样点的平均正标签数/个	每个样点的最大负标签数/个
MIRFLICKR	38	25 000	4.7	17
COREL5K	260	4 999	3.4	5
ESPGAME	268	23 641	4.7	15
IAPRTCL12	291	19 627	5.7	23

参数	数据集	ILDLR	BR	CPLST	MLRGL	FastTag
macro_F1	MIRFLICKR	0.499 6±0.005 1	0.494 1±0.004 2	0.493 2±0.004 2	0.495 6±0.007 3	0.467 9±0.005 3
	COREL5K	0.208 1±0.056 3	0.201 0±0.046 6	0.200 4±0.049 3	0.131 5±0.044 4	0.203 6±0.038 0
	ESPGAME	0.237 8±0.013 1	0.231 4±0.016 2	0.232 7±0.016 7	0.125 1±0.007 2	0.228 4±0.013
	IAPRTCL12	0.246 7±0.040 5	0.235 9±0.042 2	0.237 5±0.042 4	0.126 7±0.041 1	0.228 5±0.041 1
micro_F1	MIRFLICKR	0.467 1±0.003 4	0.461 1±0.005 5	0.461 1±0.004 7	0.466 1±0.009 2	0.436 2±0.006 1
	COREL5K	0.208 3±0.055 5	0.196 7±0.048 1	0.198 2±0.048 1	0.130 5±0.043 6	0.202 2±0.036 2
	ESPGAME	0.227 6±0.014 0	0.221±0.017 3	0.221 7±0.018 0	0.118 9±0.001 0	0.218 9±0.017 4
	IAPRTCL12	0.240 8±0.045 1	0.231 5±0.043 1	0.231 0±0.042 7	0.123 9±0.041 2	0.221 1±0.043 6

A multi-label classification method for disposing incomplete labeled data and label relevance

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