HELPFuL: Human Emotion Label Prediction Based on Fuzzy Learning for Realizing Artificial Intelligent in IoT
Abstract
:1. Introduction
2. Fuzzy Sets for Label Distribution
2.1. Fuzzy Set
2.2. Fuzzy Rough Sets (FRS)
2.3. Advantages of Fuzzy Label Distribution Learning
3. Human Emotion Label Prediction Based on Fuzzy Learning (HELPFuL)
3.1. Algorithm
Algorithm 1: Learning weights of upper and lower approximations |
Input: Fuzzy label distribution system , label set D for do end Output: and |
Algorithm 2: Fuzzy-rough-based Fuzzy label distribution classification |
Input: Fuzzy label distribution system , label set D, test instance , , and for do end Output: label set |
3.2. Example
4. Experiments
4.1. Datasets
4.2. Evaluation Measures for FLDL
4.3. Experimental Setup
4.4. Results and Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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U | C | |||
---|---|---|---|---|
0.39 | 0.64 | 0.31 | 0.72 | |
0.18 | 0.35 | 0.74 | 0.23 | |
0.35 | 0.76 | 0.42 | 0.55 | |
0.09 | 0.41 | 0.65 | 0.19 |
No. | Dataset | Instances | Features | Labels |
---|---|---|---|---|
1 | Yeast-alpha | 2465 | 24 | 18 |
2 | Yeast-cdc | 2465 | 24 | 15 |
3 | Yeast-elu | 2465 | 24 | 14 |
4 | Yeast-diau | 2465 | 24 | 7 |
5 | Yeast-heat | 2465 | 24 | 6 |
6 | Yeast-spo | 2465 | 24 | 6 |
7 | Yeast-cold | 2465 | 24 | 4 |
8 | Yeast-dtt | 2465 | 24 | 4 |
9 | Yeast-spo5 | 2465 | 24 | 3 |
10 | Yeast-spoem | 2465 | 24 | 2 |
11 | Human Gene | 30,542 | 36 | 68 |
12 | Natural Scene | 2000 | 294 | 9 |
13 | SJAFFE | 213 | 243 | 6 |
14 | SBU_3DFE | 2500 | 243 | 6 |
15 | Movie | 7755 | 1869 | 5 |
Name | Formula | |
---|---|---|
1 | Hamming distance | |
2 | Euclid distance | |
3 | Lambert distance | |
4 | Fuzzy absolute distance |
Name | Formula | |
---|---|---|
1 | Cheb | |
2 | Clark | |
3 | Cosine | |
4 | Intersection |
No. | PT-Bayes | PT-SVM | AA-BP | AA-kNN | FFRS-w |
---|---|---|---|---|---|
1 | 0.174 ± 0.011 | 0.017 ± 0.002 | 0.036 ± 0.003 | 0.015 ± 0.001 | 0.014 ± 0.001 |
2 | 0.172 ± 0.016 | 0.020 ± 0.003 | 0.037 ± 0.002 | 0.018 ± 0.001 | 0.016 ± 0.001 |
3 | 0.166 ± 0.014 | 0.019 ± 0.001 | 0.036 ± 0.002 | 0.018 ± 0.001 | 0.016 ± 0.001 |
4 | 0.167 ± 0.007 | 0.046 ± 0.004 | 0.048 ± 0.003 | 0.039 ± 0.001 | 0.038 ± 0.002 |
5 | 0.176 ± 0.018 | 0.046 ± 0.001 | 0.052 ± 0.003 | 0.045 ± 0.001 | 0.043 ± 0.001 |
6 | 0.178 ± 0.009 | 0.065 ± 0.006 | 0.067 ± 0.005 | 0.064 ± 0.002 | 0.059 ± 0.004 |
7 | 0.177 ± 0.011 | 0.057 ± 0.003 | 0.057 ± 0.003 | 0.055 ± 0.002 | 0.052 ± 0.002 |
8 | 0.177 ± 0.010 | 0.040 ± 0.001 | 0.043 ± 0.002 | 0.039 ± 0.001 | 0.037 ± 0.001 |
9 | 0.211 ± 0.011 | 0.093 ± 0.006 | 0.094 ± 0.006 | 0.096 ± 0.005 | 0.093 ± 0.006 |
10 | 0.190 ± 0.017 | 0.091 ± 0.005 | 0.089 ± 0.005 | 0.093 ± 0.004 | 0.088 ± 0.005 |
11 | 0.195 ± 0.085 | 0.054 ± 0.004 | 0.059 ± 0.004 | 0.065 ± 0.005 | 0.053 ± 0.004 |
12 | 0.407 ± 0.027 | 0.414 ± 0.036 | 0.335 ± 0.016 | 0.374 ± 0.013 | 0.350 ± 0.015 |
13 | 0.121 ± 0.016 | 0.127 ± 0.017 | 0.130 ± 0.017 | 0.114 ± 0.017 | 0.120 ± 0.016 |
14 | 0.116 ± 0.004 | 0.119 ± 0.006 | 0.113 ± 0.005 | 0.103 ± 0.003 | 0.135 ± 0.005 |
15 | 0.199 ± 0.009 | 0.213 ± 0.039 | 0.157 ± 0.013 | 0.154 ± 0.005 | 0.129 ± 0.008 |
No. | PT-Bayes | PT-SVM | AA-BP | AA-kNN | FFRS-w |
---|---|---|---|---|---|
1 | 6.382 ± 0.305 | 0.921 ± 0.107 | 2.352 ± 0.173 | 0.758 ± 0.040 | 0.685 ± 0.047 |
2 | 4.987 ± 0.222 | 0.785 ± 0.084 | 1.718 ± 0.110 | 0.717 ± 0.041 | 0.649 ± 0.042 |
3 | 4.461 ± 0.282 | 0.691 ± 0.047 | 1.488 ± 0.098 | 0.644 ± 0.016 | 0.587 ± 0.019 |
4 | 1.744 ± 0.071 | 0.528 ± 0.031 | 0.568 ± 0.033 | 0.455 ± 0.011 | 0.439 ± 0.020 |
5 | 1.415 ± 0.102 | 0.396 ± 0.016 | 0.459 ± 0.031 | 0.392 ± 0.010 | 0.371 ± 0.008 |
6 | 1.473 ± 0.069 | 0.565 ± 0.049 | 0.599 ± 0.043 | 0.559 ± 0.024 | 0.515 ± 0.033 |
7 | 0.845 ± 0.059 | 0.267 ± 0.014 | 0.268 ± 0.015 | 0.260 ± 0.013 | 0.243 ± 0.009 |
8 | 0.846 ± 0.051 | 0.186 ± 0.008 | 0.204 ± 0.012 | 0.182 ± 0.007 | 0.172 ± 0.006 |
9 | 0.681 ± 0.038 | 0.287 ± 0.019 | 0.291 ± 0.018 | 0.297 ± 0.016 | 0.285 ± 0.019 |
10 | 0.424 ± 0.038 | 0.187 ± 0.011 | 0.184 ± 0.011 | 0.191 ± 0.008 | 0.183 ± 0.010 |
11 | 34.24 ± 3.634 | 14.63 ± 0.647 | 22.79 ± 1.841 | 16.28 ± 0.818 | 14.51 ± 0.650 |
12 | 7.149 ± 0.109 | 7.208 ± 0.205 | 6.767 ± 0.095 | 3.044 ± 0.137 | 6.663 ± 0.113 |
13 | 0.904 ± 0.086 | 0.935 ± 0.074 | 1.046 ± 0.124 | 0.843 ± 0.113 | 0.889 ± 0.085 |
14 | 1.116 ± 0.020 | 1.147 ± 0.064 | 1.051 ± 0.064 | 0.841 ± 0.014 | 0.900 ± 0.041 |
15 | 1.547 ± 0.075 | 1.537 ± 0.216 | 1.269 ± 0.089 | 1.276 ± 0.046 | 1.120 ± 0.050 |
No. | PT-Bayes | PT-SVM | AA-BP | AA-kNN | FFRS-w |
---|---|---|---|---|---|
1 | 0.743 ± 0.015 | 0.991 ± 0.002 | 0.949 ± 0.006 | 0.994 ± 0.001 | 0.995 ± 0.001 |
2 | 0.766 ± 0.017 | 0.991 ± 0.002 | 0.960 ± 0.004 | 0.992 ± 0.001 | 0.993 ± 0.001 |
3 | 0.780 ± 0.018 | 0.992 ± 0.001 | 0.965 ± 0.004 | 0.993 ± 0.001 | 0.994 ± 0.001 |
4 | 0.856 ± 0.007 | 0.982 ± 0.002 | 0.979 ± 0.002 | 0.986 ± 0.001 | 0.988 ± 0.001 |
5 | 0.866 ± 0.015 | 0.986 ± 0.001 | 0.981 ± 0.003 | 0.986 ± 0.001 | 0.988 ± 0.001 |
6 | 0.859 ± 0.008 | 0.971 ± 0.005 | 0.969 ± 0.004 | 0.972 ± 0.002 | 0.977 ± 0.003 |
7 | 0.898 ± 0.008 | 0.986 ± 0.001 | 0.986 ± 0.002 | 0.987 ± 0.001 | 0.988 ± 0.001 |
8 | 0.897 ± 0.008 | 0.993 ± 0.001 | 0.991 ± 0.001 | 0.993 ± 0.001 | 0.994 ± 0.001 |
9 | 0.893 ± 0.008 | 0.973 ± 0.003 | 0.973 ± 0.003 | 0.970 ± 0.003 | 0.974 ± 0.003 |
10 | 0.914 ± 0.011 | 0.976 ± 0.003 | 0.978 ± 0.002 | 0.975 ± 0.002 | 0.978 ± 0.002 |
11 | 0.456 ± 0.089 | 0.832 ± 0.011 | 0.726 ± 0.026 | 0.766 ± 0.020 | 0.834 ± 0.011 |
12 | 0.559 ± 0.014 | 0.490 ± 0.082 | 0.697 ± 0.011 | 0.624 ± 0.016 | 0.678 ± 0.010 |
13 | 0.930 ± 0.013 | 0.920 ± 0.014 | 0.908 ± 0.019 | 0.934 ± 0.018 | 0.931 ± 0.013 |
14 | 0.924 ± 0.004 | 0.914 ± 0.006 | 0.926 ± 0.006 | 0.938 ± 0.002 | 0.920 ± 0.005 |
15 | 0.850 ± 0.008 | 0.806 ± 0.061 | 0.895 ± 0.014 | 0.880 ± 0.006 | 0.926 ± 0.009 |
No. | PT-Bayes | PT-SVM | AA-BP | AA-kNN | FFRS-w |
---|---|---|---|---|---|
1 | 0.660 ± 0.016 | 0.949 ± 0.006 | 0.877 ± 0.008 | 0.958 ± 0.002 | 0.962 ± 0.003 |
2 | 0.681 ± 0.015 | 0.948 ± 0.006 | 0.891 ± 0.007 | 0.953 ± 0.003 | 0.957 ± 0.003 |
3 | 0.695 ± 0.019 | 0.951 ± 0.003 | 0.899 ± 0.006 | 0.955 ± 0.001 | 0.959 ± 0.001 |
4 | 0.764 ± 0.008 | 0.926 ± 0.004 | 0.922 ± 0.004 | 0.937 ± 0.002 | 0.939 ± 0.003 |
5 | 0.773 ± 0.018 | 0.935 ± 0.003 | 0.925 ± 0.005 | 0.936 ± 0.002 | 0.939 ± 0.001 |
6 | 0.765 ± 0.010 | 0.906 ± 0.008 | 0.902 ± 0.007 | 0.908 ± 0.004 | 0.915 ± 0.005 |
7 | 0.802 ± 0.012 | 0.934 ± 0.004 | 0.934 ± 0.004 | 0.936 ± 0.003 | 0.940 ± 0.002 |
8 | 0.801 ± 0.011 | 0.954 ± 0.002 | 0.950 ± 0.003 | 0.955 ± 0.002 | 0.958 ± 0.001 |
9 | 0.789 ± 0.011 | 0.907 ± 0.006 | 0.906 ± 0.006 | 0.904 ± 0.005 | 0.909 ± 0.006 |
10 | 0.810 ± 0.017 | 0.909 ± 0.005 | 0.911 ± 0.005 | 0.907 ± 0.004 | 0.912 ± 0.005 |
11 | 0.470 ± 0.062 | 0.781 ± 0.010 | 0.671 ± 0.025 | 0.742 ± 0.014 | 0.784 ± 0.009 |
12 | 0.350 ± 0.014 | 0.364 ± 0.055 | 0.499 ± 0.012 | 0.544 ± 0.018 | 0.556 ± 0.014 |
13 | 0.846 ± 0.016 | 0.839 ± 0.015 | 0.824 ± 0.022 | 0.855 ± 0.021 | 0.848 ± 0.016 |
14 | 0.834 ± 0.003 | 0.827 ± 0.009 | 0.847 ± 0.008 | 0.872 ± 0.002 | 0.839 ± 0.007 |
15 | 0.725 ± 0.011 | 0.711 ± 0.052 | 0.788 ± 0.015 | 0.780 ± 0.007 | 0.864 ± 0.015 |
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Zhang, L.; Zhang, H.; Wu, Y.; Xu, Y.; Ye, T.; Ma, M.; Li, L. HELPFuL: Human Emotion Label Prediction Based on Fuzzy Learning for Realizing Artificial Intelligent in IoT. Appl. Sci. 2023, 13, 7799. https://doi.org/10.3390/app13137799
Zhang L, Zhang H, Wu Y, Xu Y, Ye T, Ma M, Li L. HELPFuL: Human Emotion Label Prediction Based on Fuzzy Learning for Realizing Artificial Intelligent in IoT. Applied Sciences. 2023; 13(13):7799. https://doi.org/10.3390/app13137799
Chicago/Turabian StyleZhang, Lingjun, Hua Zhang, Yifan Wu, Yanping Xu, Tingcong Ye, Mengjing Ma, and Linhao Li. 2023. "HELPFuL: Human Emotion Label Prediction Based on Fuzzy Learning for Realizing Artificial Intelligent in IoT" Applied Sciences 13, no. 13: 7799. https://doi.org/10.3390/app13137799