Multi-Instance Learning from Supervised View

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Multi-Instance Learning from Supervised View
Abstract In multi-instance learning, the training set comprises labeled bags that are composed of unlabeled instances, and the task is to predict the labels of unseen bags. This paper studies multi-instance learning from the view of supervised learning. First, by analyzing some representative learning algorithms, this paper shows that multi-instance learners can be derived from supervised learners by shifting their focuses from the discrimination on the instances to the discrimination on the bags. Second, considering that ensemble learning paradigms can effectively enhance supervised learners, this paper proposes to build multi-instance ensembles to solve multi-instance problems. Experiments on a real-world benchmark test show that ensemble learning paradigms can significantly enhance multi-instance learners. Keywords Machine Learning; Multi-Instance Learning; Supervised Learning; Ensemble Learning; Multi-Instance Ensemble
Zhi-Hua Zhou
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2006
Where JCST
Authors Zhi-Hua Zhou
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