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Weiwei Cheng's blog

Combining instance-based learning and logistic regression for multi-label classification

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Our recent paper on multi-label classification (co-authored by Prof. Eyke Hüllermeier) is now available at SpringerLink. You could download an unofficial draft from my homepage if no access to Springerlink is available. This work has won the Best Student Paper Award at ECML PKDD 2009, which will be held this September in Slovenia.

Image of the first page of the fulltext

Multilabel classification is an extension of conventional classification in which a single instance can be associated with multiple labels. Recent research has shown that, just like for conventional classification, instance-based learning algorithms relying on the nearest neighbor estimation principle can be used quite
successfully in this context. However, since hitherto existing algorithms do not take correlations and interdependencies between labels into account, their potential has not yet been fully exploited. In this paper, we propose a new approach to multilabel classification, which is based on a framework that unifies instance-based learning and logistic regression, comprising both methods as special cases. This approach allows one to capture interdependencies between labels and, moreover, to combine model-based and similarity-based inference for multilabel classification. As will be shown by experimental studies, our approach is able to improve predictive accuracy in terms of several evaluation criteria for multilabel prediction.

Have fun with reading!

Written by Weiwei

30/07/2009 在 00:03

发表在 学术


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