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Media type:
E-Article
Title:
Information Theoretic Multi-Target Feature Selection via Output Space Quantization
Contributor:
Sechidis, Konstantinos;
Spyromitros-Xioufis, Eleftherios;
Vlahavas, Ioannis
Published:
MDPI AG, 2019
Published in:
Entropy, 21 (2019) 9, Seite 855
Language:
English
DOI:
10.3390/e21090855
ISSN:
1099-4300
Origination:
Footnote:
Description:
<jats:p>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 an algorithm that captures these three desirable terms and is suitable for the well-known multi-target prediction settings of multi-label/dimensional classification and multivariate regression. We achieve this by combining two ideas—deriving low-order information theoretic approximations for the input space and using quantization algorithms for deriving low-dimensional approximations of the output space. Under the above framework we derive a novel criterion, Group-JMI-Rand, which captures various high-order target interactions. In an extensive experimental study we showed that our suggested criterion achieves competing performance against various other information theoretic feature selection criteria suggested in the literature.</jats:p>