Mixture of Metrics Optimization for Machine Learning Problems

• Autorzy: Wiercioch M. , Śmieja M.

Identyfikatory

DOI

10.4467/20838476SI.15.008.3030

• Języki publikacji: EN

Abstrakty

EN

The selection of data representation and metric for a given data set is one of the most crucial problems in machine learning since it affects the results of classification and clustering methods. In this paper we investigate how to combine a various data representations and metrics into a single function which better reflects the relationships between data set elements than a single representation-metric pair. Our approach relies on optimizing a linear combination of selected distance measures with use of least square approximation. The application of our method for classification and clustering of chemical compounds seems to increase the accuracy of these methods.

Słowa kluczowe

EN

metric learning; classification; clustering; chemical compound activity; fingerprint

• Wydawca: Wydawnictwo Uniwersytetu Jagiellońskiego
• Czasopismo: Schedae Informaticae
• Rocznik: 2015
• Tom: Vol. 24
• Strony: 83--92
• Opis fizyczny: Bibliogr. 27 poz., rys.

Twórcy

autor

Wiercioch M.

• Faculty of Mathematics and Computer Science ul. Lojasiewicza 6, 30-348 Kraków

autor

Śmieja M.

• Faculty of Mathematics and Computer Science ul. Lojasiewicza 6, 30-348 Kraków

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