Multi-instance learning from positive and unlabeled bags

Wu, J; Zhu, X; Zhang, C; Cai, Z

Multi-instance learning from positive and unlabeled bags

Wu, J Zhu, X Zhang, C

Cai, Z

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, 8443 LNAI (PART 1), pp. 237 - 248
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Wu, J	en_US
dc.contributor.author	Zhu, X	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.contributor.author	Cai, Z	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, 8443 LNAI (PART 1), pp. 237 - 248	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/33941
dc.description.abstract	Many methods exist to solve multi-instance learning by using different mechanisms, but all these methods require that both positive and negative bags are provided for learning. In reality, applications may only have positive samples to describe users' learning interests and remaining samples are unlabeled (which may be positive, negative, or irrelevant to the underlying learning task). In this paper, we formulate this problem as positive and unlabeled multi-instance learning (puMIL). The main challenge of puMIL is to accurately identify negative bags for training discriminative classification models. To solve the challenge, we assign a weight value to each bag, and use an Artificial Immune System based self-adaptive process to select most reliable negative bags in each iteration. For each bag, a most positive instance (for a positive bag) or a least negative instance (for an identified negative bag) is selected to form a positive margin pool (PMP). A weighted kernel function is used to calculate pairwise distances between instances in the PMP, with the distance matrix being used to learn a support vector machines classifier. A test bag is classified as positive if one or multiple instances inside the bag are classified as positive, and negative otherwise. Experiments on real-world data demonstrate the algorithm performance. © 2014 Springer International Publishing.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-319-06608-0_20	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Multi-instance learning from positive and unlabeled bags	en_US
dc.type	Conference Proceeding
utslib.citation.volume	PART 1	en_US
utslib.citation.volume	8443 LNAI	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	PART 1	en_US
pubs.publication-status	Published	en_US
pubs.volume	8443 LNAI	en_US

Abstract:

Many methods exist to solve multi-instance learning by using different mechanisms, but all these methods require that both positive and negative bags are provided for learning. In reality, applications may only have positive samples to describe users' learning interests and remaining samples are unlabeled (which may be positive, negative, or irrelevant to the underlying learning task). In this paper, we formulate this problem as positive and unlabeled multi-instance learning (puMIL). The main challenge of puMIL is to accurately identify negative bags for training discriminative classification models. To solve the challenge, we assign a weight value to each bag, and use an Artificial Immune System based self-adaptive process to select most reliable negative bags in each iteration. For each bag, a most positive instance (for a positive bag) or a least negative instance (for an identified negative bag) is selected to form a positive margin pool (PMP). A weighted kernel function is used to calculate pairwise distances between instances in the PMP, with the distance matrix being used to learn a support vector machines classifier. A test bag is classified as positive if one or multiple instances inside the bag are classified as positive, and negative otherwise. Experiments on real-world data demonstrate the algorithm performance. © 2014 Springer International Publishing.

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http://hdl.handle.net/10453/33941