Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2015: Machine Learning and Knowledge Discovery in Databases pp 351-366

Markov Blanket Discovery in Positive-Unlabelled and Semi-supervised Data

Conference paper

DOI: 10.1007/978-3-319-23528-8_22

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9284)
Cite this paper as:
Sechidis K., Brown G. (2015) Markov Blanket Discovery in Positive-Unlabelled and Semi-supervised Data. In: Appice A., Rodrigues P., Santos Costa V., Soares C., Gama J., Jorge A. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2015. Lecture Notes in Computer Science, vol 9284. Springer, Cham

Abstract

The importance of Markov blanket discovery algorithms is twofold: as the main building block in constraint-based structure learning of Bayesian network algorithms and as a technique to derive the optimal set of features in filter feature selection approaches. Equally, learning from partially labelled data is a crucial and demanding area of machine learning, and extending techniques from fully to partially supervised scenarios is a challenging problem. While there are many different algorithms to derive the Markov blanket of fully supervised nodes, the partially-labelled problem is far more challenging, and there is a lack of principled approaches in the literature. Our work derives a generalization of the conditional tests of independence for partially labelled binary target variables, which can handle the two main partially labelled scenarios: positive-unlabelled and semi-supervised. The result is a significantly deeper understanding of how to control false negative errors in Markov Blanket discovery procedures and how unlabelled data can help.

Keywords

Markov blanket discovery Partially labelled Positive unlabelled Semi supervised Mutual information 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Computer ScienceUniversity of ManchesterManchesterUK

Personalised recommendations