K. Sechidis, E. Spyromitros-Xioufis, I. Vlahavas, Multi-target feature selection through output space clustering, ESANN 2019

Authors:

  • E. Spyromitros-Xioufis, K.Sechidis, I.Vlahavas

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Abstract:

  • A key challenge in information theoretic feature selection is to estimate mutual information expressions that capture three desirable terms: the relevancy of a feature with the output, the redundancy and the complementarity between groups of features. The challenge becomes more pronounced in multi-target problems, where the output space is multi-dimensional. Our work presents a generic algorithm that captures these three desirable terms and is suitable for the well-known multi-target prediction settings of multi-label/dimensional classication and multivariate regression. We achieve this by combining two ideas: deriving low-order information theoretic approximations for the input space and using clustering for deriving low-dimensional approximations of the output space.
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School of Informatics
Aristotle University of Thessaloniki
54124 Thessaloniki, Greece

Contact

Prof. Ioannis P. Vlahavas
tel: ++30 231099-8145
fax: ++30 231099-8362
e-mail: vlahavas@csd.auth.gr

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