ML-Flex: a flexible toolbox for performing classification analyses in parallel

Authors:
Stephen R. Piccolo;Lewis J. Frey
Affiliations:
Department of Pharmacology and Toxicology, School of Pharmacy, University of Utah, Salt Lake City, UT;Huntsman Cancer Institute, Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, UT
Venue:
The Journal of Machine Learning Research
Year:
2012

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Abstract

Motivated by a need to classify high-dimensional, heterogeneous data from the bioinformatics domain, we developed ML-Flex, a machine-learning toolbox that enables users to perform two-class and multi-class classification analyses in a systematic yet flexible manner. ML-Flex was written in Java but is capable of interfacing with third-party packages written in other programming languages. It can handle multiple input-data formats and supports a variety of customizations. MLFlex provides implementations of various validation strategies, which can be executed in parallel across multiple computing cores, processors, and nodes. Additionally, ML-Flex supports aggregating evidence across multiple algorithms and data sets via ensemble learning. This open-source software package is freely available from http://mlflex.sourceforge.net.