Neural Tensor Network for Multi- Label Classification

The difference of multi-label classification from traditional classification is that an instance may associate a set of labels simultaneously. In recent study, some scholars have proposed that the information which derives from the query instance’s nearest neighbors, can be useful when predicting the labels of the query instance. On the basis of their research, we propose a new approach to multi-label classification, which employs neural tensor network (NTN) to explore the relations among the labels of neighbors and classify the query instance with these correlations. This method utilizes the correlations and interdependencies between labels and leverages the potential of data. Experiments on real data show that our method can achieve good performance in multi-label classification.

[1]  Lei Tang,et al.  Large scale multi-label classification via metalabeler , 2009, WWW '09.

[2]  Zhen Wang,et al.  Aligning Knowledge and Text Embeddings by Entity Descriptions , 2015, EMNLP.

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

[4]  Jiebo Luo,et al.  Learning multi-label scene classification , 2004, Pattern Recognit..

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

[6]  Lei Wu,et al.  Lift: Multi-Label Learning with Label-Specific Features , 2015, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Lei Zhang,et al.  Multi-label sparse coding for automatic image annotation , 2009, CVPR.

[8]  Naonori Ueda,et al.  Parametric Mixture Models for Multi-Labeled Text , 2002, NIPS.

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

[10]  Yee Whye Teh,et al.  A fast and simple algorithm for training neural probabilistic language models , 2012, ICML.

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

[12]  Eyke Hüllermeier,et al.  Combining Instance-Based Learning and Logistic Regression for Multilabel Classification , 2009, ECML/PKDD.

[13]  Grigorios Tsoumakas,et al.  Random k -Labelsets: An Ensemble Method for Multilabel Classification , 2007, ECML.

[14]  Andrew McCallum,et al.  Collective multi-label classification , 2005, CIKM '05.

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

[16]  Eisaku Maeda,et al.  Maximal Margin Labeling for Multi-Topic Text Categorization , 2004, NIPS.

[17]  Philip S. Yu,et al.  Multiple Structure-View Learning for Graph Classification , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[18]  刘景华,et al.  Multi-label feature selection based on max-dependency and min-redundancy , 2015 .

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

[20]  Yiming Yang,et al.  Multilabel classification with meta-level features , 2010, SIGIR.

[21]  Danqi Chen,et al.  Reasoning With Neural Tensor Networks for Knowledge Base Completion , 2013, NIPS.

[22]  Marcel Worring,et al.  The challenge problem for automated detection of 101 semantic concepts in multimedia , 2006, MM '06.

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

[24]  Sunita Sarawagi,et al.  Discriminative Methods for Multi-labeled Classification , 2004, PAKDD.

[25]  Jiun-Hung Chen,et al.  A multi-label classification based approach for sentiment classification , 2015, Expert Syst. Appl..

[26]  Xuelong Li,et al.  Block-Row Sparse Multiview Multilabel Learning for Image Classification , 2016, IEEE Transactions on Cybernetics.

[27]  Eyke Hüllermeier,et al.  Dependent binary relevance models for multi-label classification , 2014, Pattern Recognit..

[28]  Aapo Hyvärinen,et al.  Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics , 2012, J. Mach. Learn. Res..

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