Multi-label Learning Algorithm of Regression Kernel Extreme Learning Machine
WANG Yibin1,2 , CHENG Yusheng1,2, HE Yue1, PEI Gensheng1
1.School of Computer and Information, Anqing Normal University, Anqing 246133 2.University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, Anqing 246133
Abstract：In the multi-label learning algorithms based on extreme learning machine(ELM), the ELM classification model is often used, and the correlation between labels is ignored. Accordingly, a multi-label learning algorithm of regression kernel extreme learning machine with association rules(ML-ASRKELM) is proposed in this paper. Firstly, the rule vectors between labels are extracted by analyzing the association rules of label space. Then, the prediction results are obtained by the proposed multi-label regression kernel extreme learning machine(ML-RKELM). Eventually, if the rule vectors are not empty, the final results are calculated by the rule vectors and the prediction results of ML-KRELM. Otherwise, the final results are predicted by ML-RKELM. The experimental results show that ML-ASRKELM and ML-RKELM are superior to other algorithms, and the effectiveness of the proposed algorithms are illustrated by the statistical hypothesis test.
[1] ZHOU Z H, ZHANG M L. Multi-label Learning//SAMMUT C, WEBB G I, eds. Encyclopedia of Machine Learning and Data Mining. Berlin, Germany: Springer, 2017: 875-881. [2] ZHU X F, LI X L, ZHANG S C. Block-Row Sparse Multiview Multilabel Learning for Image Classification. IEEE Transactions on Cybernetics, 2016, 46(2): 450-461. [3] ELGHAZEL H, AUSSEM A, GHARROUDI O, et al. Ensemble Multi-label Text Categorization Based on Rotation Forest and Latent Semantic Indexing. Expert Systems with Applications, 2016, 57: 1-11. [4] ZHANG M L, ZHOU Z H. Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(10): 1338-1351. [5] ELISSEEFF A, WESTON J. A Kernel Method for Multi-labelled Classification//Proc of the 14th International Conference on Neural Information Processing Systems. Cambridge, USA: The MIT Press, 2001: 681-687. [6] TOMAR D, AGARWAL S. Multi-label Classifier for Emotion Re-cognition from Music//Proc of the 3rd International Conference on Advanced Computing, Networking and Informatics. Berlin, Germany: Springer, 2015: 111-123. [7] ZHANG M L, ZHOU Z H. ML-KNN: A Lazy Learning Approach to Multi-label Learning. Pattern Recognition, 2007, 40(7): 2038-2048. [8] ZHANG M L, WU L. LIFT: Multi-label Learning with Label-Specific Features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(1): 107-120. [9] HUANG G B, ZHU Q Y, SIEW C K. Extreme Learning Machine: Theory and Applications. Neurocomputing, 2006, 70(1/2/3): 489-501. [10] SUN X, XU J T, JIANG C M, et al. Extreme Learning Machine for Multi-label Classification. Entropy, 2016, 18(6). DOI: 10.3390/e18060225. [11] MENG J E, VENKATESAN R, NING W. A High Speed Multi-label Classifier Based on Extreme Learning Machines[C/OL]. [2017-11-25]. https://arxiv.org/ftp/arxiv/papers/1608/1608.08898.pdf. [12] VENKATESAN R, MENG J E. Multi-label Classification Method Based on Extreme Learning Machines//Proc of the International Conference on Control Automation Robotics & Vision. Washington, USA: IEEE, 2016: 619-624. [13] ZHANG N, DING S F, ZHANG J. Multi Layer ELM-RBF for Multi-label Learning. Applied Soft Computing, 2016, 43: 535-545. [14] LUO F F, GUO W Z, YU Y L, et al. A Multi-label Classification Algorithm Based on Kernel Extreme Learning Machine. Neurocomputing, 2017, 260: 313-320. [15] HUANG G B, ZHOU H M, DING X J, et al. Extreme Learning Machine for Regression and Multiclass Classification. IEEE Tran-sactions on Systems, Man, and Cybernetics(Cybernetics), 2012, 42(2): 513-529. [16] 陈 耿,朱玉全,杨鹤标,等.关联规则挖掘中若干关键技术的研究.计算机研究与发展, 2005, 42(10): 1785-1789. (CHEN G, ZHU Y Q, YANG H B, et al. Study of Some Key Techniques in Mining Association Rule. Journal of Computer Research and Development, 2005, 42(10): 1785-1789.) [17] WITTEN I H, FRANK E, HALL M A, et al. Data Mining: Practical Machine Learning Tools and Techniques. San Francisco, USA: Morgan Kaufmann, 2016. [18] ZHANG M L, ZHOU Z H. A Review on Multi-label Learning Algorithms. IEEE Transactions on Knowledge and Data Engineering,2014, 26(8): 1819-1837. [19] PARK S H, F RNKRANZ J. Multi-label Classification with Label Constraints[C/OL]. [2017-11-25]. http://www.ecmlpkdd2008.org/sites/ecmlpkdd2008.org/files/pdf/workshops/pl/11.pdf. [20] BECHINI A, MARCELLONI F, SEGATORI A. A MapReduce Solution for Associative Classification of Big Data. Information Sciences, 2016, 332: 33-55. [21] ZHANG M L. ML-RBF: RBF Neural Networks for Multi-label Learning. Neural Processing Letters, 2009, 29(2): 61-74. [22] DEMAR J. Statistical Comparisons of Classifiers over Multiple Data Sets. Journal of Machine Learning Research, 2006, 7: 1-30.
2018, Vol. 31 
