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New Approach for Joint Multilabel Classification with Community-Aware Label Graph Learning Technique

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Authors:
R. V. Argiddi, Disha Rajan Shah
10.5120/ijca2018916892

R V Argiddi and Disha Rajan Shah. New Approach for Joint Multilabel Classification with Community-Aware Label Graph Learning Technique. International Journal of Computer Applications 180(36):1-7, April 2018. BibTeX

@article{10.5120/ijca2018916892,
	author = {R. V. Argiddi and Disha Rajan Shah},
	title = {New Approach for Joint Multilabel Classification with Community-Aware Label Graph Learning Technique},
	journal = {International Journal of Computer Applications},
	issue_date = {April 2018},
	volume = {180},
	number = {36},
	month = {Apr},
	year = {2018},
	issn = {0975-8887},
	pages = {1-7},
	numpages = {7},
	url = {http://www.ijcaonline.org/archives/volume180/number36/29295-2018916892},
	doi = {10.5120/ijca2018916892},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Multi-label classification is a significant machine learning task in which one allocates a subset of candidate labels to an object. A new multi-label classification technique based on Conditional Bernoulli Mixtures. Exploiting label dependency for multi-label image classification cans considerably develop classification performance. Probabilistic Graphical Models are one of the primary methods for demonstrating such dependences. The structure of graphical models, however, is which ever resolute heuristically or learned from very inadequate information. Moreover, neither of these methodologies scales well to large or complex graphs. We recommend a principled way to learn the structure of a graphical model by in view of input features and labels, composed with loss functions. We formulate this problem into a max-margin framework primarily, and then convert it into a convex programming problem. In conclusion, we suggest a highly scalable technique that activates a set of cliques iteratively. Our methodology exhibits both strong theoretical properties and a substantial performance development over state-of-the-art approaches on both synthetic and real-world data sets. Our proposed system has numerous attractive properties: it captures label dependences; it decreases the multi-label problem to numerous standard binary and multi-class problems; it subsumes the classic independent binary prediction and power-set subset prediction approaches as special cases; and it exhibitions accuracy and/or computational complexity benefits over present approaches. We demonstrate two implementations of our technique by means of logistic regressions and gradient boosted trees, organized with a simple training procedure centered on Expectation Maximization. We promote derive an efficient prediction procedure centered on dynamic programming, thus avoiding the cost of scrutinizing an exponential number of probable label subsets. For the testing we will use and show the efficiency of the proposed method in contradiction of competitive substitutes on benchmark datasets with image as well as pdf.

References

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Keywords

Multi graph learning, Document classification approach, semi supervised learning.