One-side probability machine: Learning imbalanced classifiers locally and globally

Rui Zhang, Kaizhu Huang

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

2 Citations (Scopus)


Imbalanced learning is a challenged task in machine learning, where the data associated with one class are far fewer than those associated with the other class. In this paper, we propose a novel model called One-Side Probability Machine (OSPM) able to learn from imbalanced data rigorously and accurately. In particular, OSPM can lead to a rigorous treatment on biased or imbalanced classification tasks, which is significantly different from previous approaches. Importantly, the proposed OSPM exploits the reliable global information from one side only, i.e., the majority class , while engaging the robust local learning [2] from the other side, i.e., the minority class. Such setting proves much effective than other models such as Biased Minimax Probability Machine (BMPM). To our best knowledge, OSPM presents the first model capable of learning from imbalanced data both locally and globally. Our proposed model has also established close connections with various famous models such as BMPM and Support Vector Machine. One appealing feature is that the optimization problem involved can be cast as a convex second order conic programming problem with a global optimum guaranteed. A series of experiments on three data sets demonstrate the advantages of our proposed method against four competitive approaches.

Original languageEnglish
Title of host publicationNeural Information Processing - 20th International Conference, ICONIP 2013, Proceedings
Number of pages8
EditionPART 2
Publication statusPublished - 2013
Event20th International Conference on Neural Information Processing, ICONIP 2013 - Daegu, Korea, Republic of
Duration: 3 Nov 20137 Nov 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume8227 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference20th International Conference on Neural Information Processing, ICONIP 2013
Country/TerritoryKorea, Republic of

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