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Learning from positive and unlabelled examples using maximum margin clustering

Chaudhari, Sneha and Shevade, Shirish (2012) Learning from positive and unlabelled examples using maximum margin clustering. In: ICONIP 2012 19th International Conference, November 12-15, 2012, Doha, Qatar.

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Official URL: http://dx.doi.org/10.1007/978-3-642-34487-9_56


Learning from Positive and Unlabelled examples (LPU) has emerged as an important problem in data mining and information retrieval applications. Existing techniques are not ideally suited for real world scenarios where the datasets are linearly inseparable, as they either build linear classifiers or the non-linear classifiers fail to achieve the desired performance. In this work, we propose to extend maximum margin clustering ideas and present an iterative procedure to design a non-linear classifier for LPU. In particular, we build a least squares support vector classifier, suitable for handling this problem due to symmetry of its loss function. Further, we present techniques for appropriately initializing the labels of unlabelled examples and for enforcing the ratio of positive to negative examples while obtaining these labels. Experiments on real-world datasets demonstrate that the non-linear classifier designed using the proposed approach gives significantly better generalization performance than the existing relevant approaches for LPU.

Item Type: Conference Paper
Publisher: Springer
Additional Information: Copyright of this article belongs to Springer.
Keywords: Learning from Positive and Unlabelled Examples; Maximum Margin Clustering; Least Squares Support Vector Classifier
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 22 Nov 2013 11:32
Last Modified: 22 Nov 2013 11:32
URI: http://eprints.iisc.ac.in/id/eprint/47815

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