NDLI: Sparse learning of maximum likelihood model for optimization of complex loss function

Content Provider	Springer Nature Link
Author	Zhang, Ning Chandrasekar, Prathamesh
Copyright Year	2015
Abstract	Traditional machine learning methods usually minimize a simple loss function to learn a predictive model and then use a complex performance measure to measure the prediction performance. However, minimizing a simple loss function cannot guarantee an optimal performance. In this paper, we study the problem of optimizing the complex performance measure directly to obtain a predictive model. We proposed to construct a maximum likelihood model for this problem, and to learn the model parameter, we minimize a complex loss function corresponding to the desired complex performance measure. To optimize the loss function, we approximate the upper bound of the complex loss. We also propose to impose the sparsity to the model parameter to obtain a sparse model. An objective was constructed by combining the upper bound of the loss function and the sparsity of the model parameter, and we develop an iterative algorithm to minimize it by using the fast iterative shrinkage-thresholding algorithm framework. The experiments on optimization on three different complex performance measures, including F-score, receiver operating characteristic curve, and recall precision curve break-even point, over three real-world applications, aircraft event recognition of civil aviation safety, intrusion detection in wireless mesh networks, and image classification, show the advantages of the proposed method over state-of-the-art methods.
Starting Page	1057
Ending Page	1067
Page Count	11
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	28
Issue Number	5
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2015-11-21
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Machine learning Complex multivariate performance Sparse learning Maximum likelihood Civil aviation safety Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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