Gravitation Theory Based Model for Multi-Label Classification

The past decade has witnessed the growing popularity in multi-label classification algorithms in the fields like text categorization, music information retrieval, and the classification of videos and medical proteins. In the meantime, the methods based on the principle of universal gravitation have been extensively used in the classification of machine learning owing to simplicity and high performance. In light of the above, this paper proposes a novel multi-label classification algorithm called the interaction and data gravitation-based model for multi-label classification (ITDGM). The algorithm replaces the interaction between two objects with the attraction between two particles. The author carries out a series of experiments on five multi-label datasets. The experimental results show that the ITDGM performs better than some well-known multi-label classification algorithms. The effect of the proposed model is assessed by the example-based F1-Measure and Label-based micro F1-measure.

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

[2]  L. Chen,et al.  Cognitive gravitation model for classification on small noisy data , 2013, Neurocomputing.

[3]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .

[4]  Yoav Freund,et al.  Boosting a weak learning algorithm by majority , 1990, COLT '90.

[5]  Luigi Fortuna,et al.  INTEGRATION OF COMPLEX NETWORKS FOR URBAN ENERGY MAPPING , 2015 .

[6]  Sadoghi Yazdi Hadi,et al.  Gravitation based classification , 2013 .

[7]  Grigorios Tsoumakas,et al.  Protein Classification with Multiple Algorithms , 2005, Panhellenic Conference on Informatics.

[8]  Grigorios Tsoumakas,et al.  An Empirical Study of Lazy Multilabel Classification Algorithms , 2008, SETN.

[9]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[10]  Chen Wang,et al.  Improving Nearest Neighbor Classification with Simulated Gravitational Collapse , 2005, ICNC.

[11]  Thierry Denoeux,et al.  Multi-label classification algorithm derived from K-nearest neighbor rule with label dependencies , 2008, 2008 16th European Signal Processing Conference.

[12]  Shreyash Tambe,et al.  EFFECTIVE DATA MINING USING NEURAL NETWORKS , 2016 .

[13]  Olfa Nasraoui,et al.  A New Gravitational Clustering Algorithm , 2003, SDM.

[14]  Armen Aghajanyan,et al.  Gravitational Clustering , 2015, ArXiv.

[15]  Li Junlin,et al.  Data classification based on supporting data gravity , 2009, 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[16]  Hanxing Liu,et al.  A DGC-Based Data Classification Method Used for Abnormal Network Intrusion Detection , 2006, ICONIP.

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

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

[19]  Sebastián Ventura,et al.  Effective lazy learning algorithm based on a data gravitation model for multi-label learning , 2016, Inf. Sci..

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

[21]  Geoff Holmes,et al.  Classifier chains for multi-label classification , 2009, Machine Learning.

[22]  Yuehui Chen,et al.  A new approach for imbalanced data classification based on data gravitation , 2014, Inf. Sci..

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

[24]  Peng Wu,et al.  STUDY ON SVM TEMPERATURE COMPENSATION OF LIQUID AMMONIA VOLUMETRIC FLOWMETER BASED ON VARIABLE WEIGHT PSO , 2015 .

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