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《山东大学学报(理学版)》 ›› 2024, Vol. 59 ›› Issue (3): 107-117.doi: 10.6040/j.issn.1671-9352.2.2023.027

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基于属性加权的ML-KNN方法

温欣1(),李德玉1,2,*()   

  1. 1. 山西大学计算机与信息技术学院, 山西 太原 030006
    2. 山西大学计算智能与中文信息处理教育部重点实验室, 山西 太原 030006
  • 收稿日期:2023-05-29 出版日期:2024-03-20 发布日期:2024-03-06
  • 通讯作者: 李德玉 E-mail:1368661957@qq.com;lidysxu@163.com
  • 作者简介:温欣(1994—), 女, 博士研究生, 研究方向为多标记学习. E-mail: 1368661957@qq.com
  • 基金资助:
    国家自然科学基金资助项目(62072294)

The ML-KNN method based on attribute weighting

Xin WEN1(),Deyu LI1,2,*()   

  1. 1. School of Computer and Information Technology, Shanxi University, Taiyuan 030006, Shanxi, China
    2. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006, Shanxi, China
  • Received:2023-05-29 Online:2024-03-20 Published:2024-03-06
  • Contact: Deyu LI E-mail:1368661957@qq.com;lidysxu@163.com

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摘要：

提出了一种基于属性加权的ML-KNN方法。首先使用变精度邻域粗糙集识别来自每一个标记的决策类非正域中的样本, 并构造异质样本对; 然后基于属性对异质样本对的区分能力评估不同属性对于分类的重要度; 最后计算样本之间的加权距离获得其近邻分布, 且基于最大化后验概率的原则实现多标记分类。在10个公开的多标记数据集上的实验结果验证了所提方法的有效性。

关键词: 多标记分类, 属性重要度, 邻域粗糙集, 分类不确定性, 异质样本对

Abstract:

A ML-KNN method based on attribute weighting has been proposed. To be specific, we first identify samples from the non-positive regions of decision classes by means of the variable precision neighborhood rough set model with respect to each label and construct the heterogeneous sample pairs. Then, the significance of different attributes for classification is evaluated based on their discernibility for the heterogeneous sample pairs. Finally, the weighted distances between samples are calculated in order to obtain the nearest neighbor distributions of samples. At the same time, based on the principle of maximizing the posterior probability, the multi-label classification is implemented. Further, the experimental results on ten public multi-label data sets verify the effectiveness of the proposed method.

Key words: multi-label classification, attribute significance, neighborhood rough set, uncertainty of classification, heterogeneous sample pair

中图分类号: 

  • TP391

表1

数据集描述"

Number Data set Sample Attribute Label Domain
1 GpositivePseAAC 519 44 0 4 biology
2 Emotions 593 72 6 music
3 Medical 978 1 449 45 text
4 Water-quality 1 060 16 14 chemistry
5 Image 2 000 294 5 image
6 Scene 2 407 294 6 image
7 Yeast 2 417 103 14 biology
8 Business 5 000 438 30 text
9 Yelp 10 810 671 5 text
10 Mediamill 43 907 120 101 video

表2

邻域参数δ的变化范围"

Data set δ
GpositivePseAAC 4.00~4.35
Emotions 1.30~1.55
Medical 2.80~3.05
Water-quality 1.50~1.75
Image 4.30~4.60
Scene 2.75~3.00
Yeast 1.25~1.50
Business 1.70~1.95
Yelp 6.00~6.25
Mediamill 2.00~2.35

图1

GpositivePseAAC数据集中NRS_MLKNN在不同参数下的分类性能"

表3

7种算法在GpositivePseAAC数据集上的分类性能"

Method HL↓ RL↓ OE↓ CV↓ AP↑
MLRS 0.163 8±0.030 3 0.160 6±0.024 1 0.323 7±0.046 7 0.487 5±0.063 1 0.813 7±0.026 1
LPLC 0.164 6±0.024 2 0.162 0±0.036 7 0.285 2±0.059 4 0.464 4±0.107 1 0.824 8±0.036 5
ML-KNN 0.155 1±0.026 7 0.157 2±0.029 6 0.310 2±0.059 6 0.479 9±0.084 4 0.819 5±0.033 6
Stacked_KNN 0.148 3±0.035 2 0.159 1±0.037 9 0.314 0±0.061 3 0.487 5±0.113 6 0.817 5±0.037 8
LAMLKNN 0.154 1±0.029 0 0.149 3±0.025 7 0.292 9±0.055 7 0.454 7±0.073 7 0.829 5±0.028 9
ML_RKNN 0.248 1±0.028 5 0.583 3±0.077 2 0.233 3±0.044 1 0.977 2±0.147 6 0.675 7±0.045 4
NRS_MLKNN 0.147 4±0.029 4 0.146 9±0.030 2 0.289 0±0.064 4 0.449 0±0.087 4 0.831 6±0.035 3

表4

7种算法在Emotions数据集上的分类性能"

Method HL↓ RL↓ OE↓ CV↓ AP↑
MLRS 0.193 0±0.016 0 0.169 6±0.023 9 0.263 1±0.055 7 1.804 2±0.146 7 0.801 4±0.027 4
LPLC 0.202 5±0.022 6 0.159 5±0.026 1 0.273 1±0.040 8 1.768 4±0.160 7 0.802 3±0.023 5
ML-KNN 0.192 5±0.017 2 0.162 1±0.017 3 0.266 6±0.030 5 1.797 5±0.088 5 0.799 6±0.015 5
Stacked_KNN 0.198 6±0.024 1 0.172 7±0.026 8 0.268 2±0.054 5 1.848 2±0.155 4 0.793 5±0.031 9
LAMLKNN 0.195 0±0.014 8 0.159 5±0.023 3 0.283 2±0.057 4 1.762 0±0.134 2 0.800 3±0.026 5
ML_RKNN 0.323 2±0.032 4 0.339 9±0.059 4 0.379 4±0.052 6 2.664 3±0.334 6 0.686 5±0.040 4
NRS_MLKNN 0.195 0±0.012 1 0.159 2±0.012 3 0.263 2±0.036 3 1.777 3±0.081 5 0.803 5±0.014 7

表5

7种算法在Medical数据集上的分类性能"

Method HL↓ RL↓ OE↓ CV↓ AP↑
MLRS 0.018 7±0.002 3 0.104 3±0.024 7 0.338 5±0.044 1 3.615 7±1.078 1 0.745 6±0.033 9
LPLC 0.018 8±0.00 2 0.079 5±0.015 9 0.283 3±0.036 7 4.401 5±1.092 2 0.757 8±0.037 8
ML-KNN 0.015 6±0.002 1 0.042 0±0.011 4 0.249 6±0.041 7 2.745 1±0.818 7 0.808 3±0.030 5
Stacked_KNN 0.01 5±0.002 1 0.057 6±0.015 5 0.248 5±0.037 3 3.551 4±1.052 9 0.791 0±0.027 2
LAMLKNN 0.015 9±0.002 1 0.037 4±0.010 5 0.244 5±0.042 2 2.225 2±0.697 5 0.816 5±0.029 4
ML_RKNN 0.052 2±0.006 7 0.431 0±0.048 3 0.273 0±0.033 3 13.564 3±2.00 4 0.522 4±0.033 9
NRS_MLKNN 0.014 0±0.002 2 0.042 4±0.011 2 0.220 9±0.032 0 2.795 5±0.787 2 0.820 4±0.024 0

表6

7种算法在Water-quality数据集上的分类性能"

Method HL↓ RL↓ OE↓ CV↓ AP↑
MLRS 0.340 8±0.009 8 0.297 8±0.016 1 0.336 9±0.050 4 9.174 5±0.206 0 0.645 7±0.022 6
LPLC 0.316 3±0.009 1 0.263 4±0.015 8 0.284 6±0.042 4 8.889 6±0.220 1 0.684 5±0.019 2
ML-KNN 0.292 0±0.011 2 0.259 4±0.013 5 0.293 2±0.052 4 8.776 4±0.241 2 0.689 8±0.020 2
Stacked_KNN 0.297 1±0.009 3 0.266 7±0.016 7 0.319 7±0.047 6 8.837 7±0.193 7 0.677 5±0.020 1
LAMLKNN 0.294 7±0.008 9 0.261 8±0.014 0 0.279 0±0.033 7 8.853 8±0.278 7 0.688 3±0.018 9
ML_RKNN 0.404 4±0.020 5 0.385 3±0.017 2 0.423 2±0.041 4 10.317 9±0.241 2 0.590 0±0.018 2
NRS_MLKNN 0.290 4±0.009 7 0.259 7±0.016 4 0.279 9±0.046 2 8.776 4±0.256 9 0.691 5±0.022 5

表7

7种算法在Image数据集上的分类性能"

Method HL↓ RL↓ OE↓ CV↓ AP↑
MLRS 0.175 4±0.014 7 0.186 8±0.019 6 0.333 5±0.034 1 0.978 0±0.103 8 0.786 2±0.020 4
LPLC 0.178 4±0.014 2 0.196 8±0.020 7 0.330 0±0.028 7 0.999 5±0.099 3 0.780 8±0.017 1
ML-KNN 0.170 1±0.014 1 0.176 5±0.020 2 0.319 5±0.033 2 0.978 0±0.103 4 0.790 0±0.020 3
Stacked_KNN 0.176 5±0.016 2 0.188 0±0.023 2 0.333 0±0.030 0 1.018 0±0.115 7 0.780 6±0.022 1
LAMLKNN 0.170 8±0.015 3 0.177 2±0.020 4 0.321 0±0.032 3 0.983 0±0.112 8 0.788 5±0.020 8
ML_RKNN 0.287 1±0.013 9 0.317 4±0.025 9 0.378 0±0.030 3 1.346 5±0.096 4 0.716 7±0.020 3
NRS_MLKNN 0.171 7±0.015 7 0.174 7±0.021 6 0.320 0±0.036 1 0.968 5±0.112 1 0.791 5±0.021 9

表8

7种算法在Scene数据集上的分类性能"

Method HL↓ RL↓ OE↓ CV↓ AP↑
MLRS 0.092 3±0.006 1 0.099 2±0.011 5 0.253 0±0.017 1 0.539 3±0.064 4 0.848 6±0.011 7
LPLC 0.096 5±0.006 5 0.090 8±0.010 6 0.250 5±0.021 8 0.519 8±0.062 6 0.847 0±0.013 1
ML-KNN 0.085 2±0.008 2 0.076 8±0.009 1 0.226 0±0.015 9 0.470 7±0.059 3 0.866 5±0.009 9
Stacked_KNN 0.087 9±0.005 5 0.085 3±0.008 6 0.232 2±0.013 8 0.515 6±0.056 8 0.859 1±0.008 6
LAMLKNN 0.085 5±0.006 7 0.074 0±0.008 7 0.225 2±0.011 8 0.455 8±0.052 6 0.867 8±0.008 4
ML_RKNN 0.164 9±0.008 9 0.254 7±0.031 3 0.286 2±0.030 4 1.108 8±0.108 2 0.761 1±0.020 9
NRS_MLKNN 0.084 7±0.006 4 0.075 4±0.009 4 0.220 2±0.015 4 0.463 7±0.061 8 0.869 5±0.010 4

表9

7种算法在Yeast数据集上的分类性能"

Method HL↓ RL↓ OE↓ CV↓ AP↑
MLRS 0.204 4±0.009 0 0.181 5±0.009 0 0.240 0±0.020 6 6.391 6±0.213 2 0.748 2±0.013 7
LPLC 0.204 0±0.012 5 0.168 9±0.009 7 0.229 2±0.029 5 6.311 6±0.187 1 0.762 4±0.018 4
ML-KNN 0.192 7±0.006 6 0.164 3±0.008 7 0.230 5±0.026 0 6.202 4±0.168 9 0.765 8±0.013 7
Stacked_KNN 0.198 5±0.009 9 0.179 3±0.009 2 0.254 9±0.030 3 6.509 0±0.125 7 0.749 1±0.017 1
LAMLKNN 0.193 8±0.007 0 0.165 1±0.008 4 0.225 9±0.022 1 6.222 7±0.153 2 0.765 1±0.013 1
ML_RKNN 0.375 9±0.018 2 0.381 3±0.021 1 0.467 4±0.034 7 9.080 2±0.218 8 0.575 1±0.021 1
NRS_MLKNN 0.192 8±0.006 6 0.163 4±0.008 3 0.227 6±0.021 8 6.196 2±0.164 5 0.767 7±0.013 3

表10

7种算法在Business数据集上的分类性能"

Method HL↓ RL↓ OE↓ CV↓ AP↑
MLRS 0.027 7±0.001 7 0.117 7±0.013 6 0.125 6±0.019 6 4.630 6±0.443 7 0.854 4±0.016 2
LPLC 0.026 7±0.001 9 0.061 2±0.004 7 0.124 6±0.021 0 3.393 0±0.235 9 0.862 6±0.015 9
ML-KNN 0.026 9±0.001 7 0.040 0±0.004 6 0.119 4±0.017 1 2.255 2±0.171 4 0.879 1±0.013 1
Stacked_KNN 0.026 1±0.001 3 0.038 7±0.003 2 0.109 6±0.013 4 2.247 8±0.148 0 0.883 4±0.009 5
LAMLKNN 0.026 8±0.001 8 0.040 1±0.004 6 0.119 2±0.019 3 2.265 4±0.171 7 0.879 3±0.013 6
ML_RKNN 0.110 9±0.003 8 0.397 8±0.023 8 0.485 4±0.029 9 13.596 4±0.845 9 0.468 3±0.014 8
NRS_MLKNN 0.026 6±0.001 7 0.038 9±0.003 6 0.115 0±0.017 3 2.217 0±0.138 1 0.881 6±0.012 0

表11

7种算法在Yelp数据集上的分类性能"

Method HL↓ RL↓ OE↓ CV↓ AP↑
MLRS 0.226 4 0.370 1 0.540 1 0.814 3 0.644 8
LPLC 0.231 4 0.331 7 0.475 8 0.830 6 0.660 9
ML-KNN 0.179 8 0.282 1 0.515 9 0.707 7 0.672 1
Stacked_KNN 0.234 5 0.335 3 0.507 6 0.890 3 0.652 6
LAMLKNN 0.180 4 0.266 8 0.500 7 0.667 3 0.683 5
ML_RKNN 0.174 8 0.936 6 0.049 0 0.954 1 0.595 6
NRS_MLKNN 0.177 4 0.276 5 0.499 3 0.693 9 0.681 8

表12

7种算法在Mediamill数据集上的分类性能"

Method HL↓ RL↓ OE↓ CV↓ AP↑
MLRS 0.032 8 0.156 7 0.168 4 28.658 7 0.676 7
LPLC 0.035 8 0.091 3 0.150 3 28.761 5 0.682 0
ML-KNN 0.031 5 0.055 0 0.147 3 18.645 6 0.703 4
Stacked_KNN 0.035 0 0.065 0 0.163 7 20.666 7 0.677 6
LAMLKNN 0.031 6 0.053 3 0.148 0 17.907 1 0.703 2
ML_RKNN 0.044 1 0.700 8 0.065 3 57.822 1 0.305 4
NRS_MLKNN 0.031 4 0.055 0 0.146 7 18.642 0 0.703 5

图2

不同方法在10个数据集中5个评估指标的平均排序"

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