ML-SLSTSVM: a new structural least square twin support vector machine for multi-label learning

Multi-label learning (MLL) is a special supervised learning task, where any single instance possibly belongs to several classes simultaneously. Nowadays, MLL methods are increasingly required by modern applications, such as protein function classification, speech recognition and textual data classification. In this paper, a structural least square twin support vector machine (SLSTSVM) classifier for multi-label learning is presented. This proposed ML - SLSTSVM focuses on the cluster-based structural information of the corresponding class in each optimization problem, which is vital for designing a good classifier in different real-world problems. This method is extended to a nonlinear version by the kernel trick. Experimental results demonstrate that proposed method is superior in generalization performance to other classifiers.

[1]  Shengcai Liao,et al.  Multi-label convolutional neural network based pedestrian attribute classification , 2017, Image Vis. Comput..

[2]  Min-Ling Zhang,et al.  A Review on Multi-Label Learning Algorithms , 2014, IEEE Transactions on Knowledge and Data Engineering.

[3]  Madan Gopal,et al.  Least squares twin support vector machines for pattern classification , 2009, Expert Syst. Appl..

[4]  Eyke Hüllermeier,et al.  Multilabel classification via calibrated label ranking , 2008, Machine Learning.

[5]  Yong Shi,et al.  Structural twin support vector machine for classification , 2013, Knowl. Based Syst..

[6]  Jason Weston,et al.  Label Partitioning For Sublinear Ranking , 2013, ICML.

[7]  Jinhui Tang,et al.  Weakly Supervised Deep Matrix Factorization for Social Image Understanding , 2017, IEEE Transactions on Image Processing.

[8]  Zhi-Hua Zhou,et al.  ML-KNN: A lazy learning approach to multi-label learning , 2007, Pattern Recognit..

[9]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[10]  Amanda Clare,et al.  Knowledge Discovery in Multi-label Phenotype Data , 2001, PKDD.

[11]  Reshma Khemchandani,et al.  Twin Support Vector Machines for Pattern Classification , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Saso Dzeroski,et al.  Decision trees for hierarchical multi-label classification , 2008, Machine Learning.

[13]  Jiajun Wu,et al.  Deep multiple instance learning for image classification and auto-annotation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Prateek Jain,et al.  Sparse Local Embeddings for Extreme Multi-label Classification , 2015, NIPS.

[15]  Yuan-Hai Shao,et al.  Improvements on Twin Support Vector Machines , 2011, IEEE Transactions on Neural Networks.

[16]  Rouslan A. Moro,et al.  Support Vector Machines (SVM) as a Technique for Solvency Analysis , 2008 .

[17]  Hongyuan Zha,et al.  Deep Extreme Multi-label Learning , 2017, ICMR.

[18]  Jie Li,et al.  A multi-label learning method for efficient affective detection , 2017, 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI).

[19]  Grigorios Tsoumakas,et al.  Multi-Label Classification: An Overview , 2007, Int. J. Data Warehous. Min..

[20]  Qiang Yang,et al.  Music Emotion Recognition by Multi-label Multi-layer Multi-instance Multi-view Learning , 2014, ACM Multimedia.

[21]  Min-Ling Zhang,et al.  Ml-rbf: RBF Neural Networks for Multi-Label Learning , 2009, Neural Processing Letters.

[22]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[23]  Tao Mei,et al.  Deep Collaborative Embedding for Social Image Understanding , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Michael Rabadi,et al.  Kernel Methods for Machine Learning , 2015 .

[25]  Jianhua Xu,et al.  Fast multi-label core vector machine , 2013, Pattern Recognit..

[26]  Zhi-Hua Zhou,et al.  Genome-Wide Protein Function Prediction through Multi-Instance Multi-Label Learning , 2014, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[27]  Jason Weston,et al.  A kernel method for multi-labelled classification , 2001, NIPS.

[28]  Eyke Hüllermeier,et al.  Label ranking by learning pairwise preferences , 2008, Artif. Intell..

[29]  Yoram Singer,et al.  BoosTexter: A Boosting-based System for Text Categorization , 2000, Machine Learning.

[30]  Grigorios Tsoumakas,et al.  MULAN: A Java Library for Multi-Label Learning , 2011, J. Mach. Learn. Res..

[31]  Manik Varma,et al.  FastXML: a fast, accurate and stable tree-classifier for extreme multi-label learning , 2014, KDD.

[32]  Xia Sun,et al.  ELM-ML: Study on Multi-label Classification Using Extreme Learning Machine , 2016 .

[33]  Sebastián Ventura,et al.  Effective active learning strategy for multi-label learning , 2018, Neurocomputing.

[34]  Heikki Huttunen,et al.  Multi-label vs. combined single-label sound event detection with deep neural networks , 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).

[35]  Feiping Nie,et al.  SVM based multi-label learning with missing labels for image annotation , 2018, Pattern Recognit..

[36]  Yan Yan,et al.  Multi-label learning based deep transfer neural network for facial attribute classification , 2018, Pattern Recognit..

[37]  Mohammad S. Sorower.  A Literature Survey on Algorithms for Multi-label Learning , 2010 .

[38]  Qiang Yang,et al.  Structural Regularized Support Vector Machine: A Framework for Structural Large Margin Classifier , 2011, IEEE Transactions on Neural Networks.

[39]  Robert E. Schapire,et al.  Hierarchical multi-label prediction of gene function , 2006, Bioinform..

[40]  Zhi-Hua Zhou,et al.  Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization , 2006, IEEE Transactions on Knowledge and Data Engineering.

[41]  Dacheng Tao,et al.  Robust Extreme Multi-label Learning , 2016, KDD.

[42]  Michael Collins,et al.  Learning Label Embeddings for Nearest-Neighbor Multi-class Classification with an Application to Speech Recognition , 2009, NIPS.

[43]  Jalal A. Nasiri,et al.  Least squares twin multi-class classification support vector machine , 2015, Pattern Recognit..

[44]  Qiuwen Zhang,et al.  MultiP-SChlo: Multi-label protein subchloroplast localization prediction , 2014, 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[45]  Yuan-Hai Shao,et al.  MLTSVM: A novel twin support vector machine to multi-label learning , 2016, Pattern Recognit..

[46]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .

[47]  Glenn Fung,et al.  Proximal support vector machine classifiers , 2001, KDD '01.

[48]  Myoung-Wan Koo,et al.  Convolutional Neural Network using a threshold predictor for multi-label speech act classification , 2017, 2017 IEEE International Conference on Big Data and Smart Computing (BigComp).

[49]  Grigorios Tsoumakas,et al.  Multi-Label Classification of Music into Emotions , 2008, ISMIR.