NDLI: Online classifier adaptation for cost-sensitive learning

Content Provider	Springer Nature Link
Author	Zhang, Junlin García, José
Copyright Year	2015
Abstract	In this paper, we propose the problem of online cost-sensitive classifier adaptation and the first algorithm to solve it. We assume that we have a base classifier for a cost-sensitive classification problem, but it is trained with respect to a cost setting different to the desired one. Moreover, we also have some training data samples streaming to the algorithm one by one. The problem is to adapt the given base classifier to the desired cost setting using the steaming training samples online. To solve this problem, we propose to learn a new classifier by adding an adaptation function to the base classifier, and update the adaptation function parameter according to the streaming data samples. Given an input data sample and the cost of misclassifying it, we update the adaptation function parameter by minimizing cost-weighted hinge loss and respecting previous learned parameter simultaneously. The proposed algorithm is compared to both online and off-line cost-sensitive algorithms on two cost-sensitive classification problems, and the experiments show that it not only outperforms them on classification performances, but also requires significantly less running time.
Starting Page	781
Ending Page	789
Page Count	9
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	27
Issue Number	3
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2015-04-01
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Cost-sensitive learning Classifier adaptation Online learning Fact detection Car detection 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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