NDLI: A multi-view two-level classification method for generalized multi-instance problems

Content Provider	IEEE Xplore Digital Library
Author	Xiaoguang Wang Xuan Liu Matwin, S. Japkowicz, N. Hongyu Guo
Copyright Year	2014
Description	Author affiliation: Sch. of Electr. Eng. & Comput. Sci., Univ. of Ottawa, Ottawa, ON, Canada (Japkowicz, N.) \|\| Nat. Res. Council of Canada, Ottawa, ON, Canada (Hongyu Guo) \|\| Fac. of Comput. Sci., Dalhousie Univ., Halifax, NS, Canada (Xiaoguang Wang; Xuan Liu; Matwin, S.)
Abstract	Multi-instance (MI) learning is different than standard propositional classification, as it uses a set of bags containing many instances as input. While the instances in each bag are not labeled, the bags themselves are, as positive or negative. In this paper, we present a novel multi-view, two-level classification framework to address the generalized multi-instance problems. We first apply supervised and unsupervised learning methods to transform a MI dataset into a multi-view, single meta-instance dataset. Then we develop a multi-view learning approach that can integrate the information acquired by individual view learners on the meta-instance dataset from the previous step, and construct a final model. Our empirical studies show that the proposed method performs well compared to other popular MI learning methods.
Starting Page	104
Ending Page	111
File Size	1275386
Page Count	8
File Format	PDF
ISBN	9781479956661
DOI	10.1109/BigData.2014.7004363
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Supervised learning Clustering algorithms Prediction algorithms Decision trees Kernel Standards Unsupervised learning
Content Type	Text
Resource Type	Article

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