Title :
Intelligible machine learning with malibu
Author :
Langlois, Robert E. ; Lu, Hui
Author_Institution :
Department of Bioengineering, University of Illinois at Chicago, 60607, USA
Abstract :
malibu is an open-source machine learning work-bench developed in C/C++ for high-performance real-world applications, namely bioinformatics and medical informatics. It leverages third-party machine learning implementations for more robust bug free software. This workbench handles several well-studied supervised machine learning problems including classification, regression, importance-weighted classification and multiple-instance learning. The malibu interface was designed to create reproducible experiments ideally run in a remote and/or command line environment. The software can be found at: http://proteomics.bioengr. uic.edu/malibu/index.html
Keywords :
Application software; Bioinformatics; Biomedical engineering; Computer languages; Java; Machine learning; Machine learning algorithms; Open source software; Performance evaluation; Reproducibility of results; Algorithms; Artificial Intelligence; Computational Biology; Database Management Systems; Databases, Factual; Humans; Information Storage and Retrieval; Programming Languages; Software; User-Computer Interface;
Conference_Titel :
Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE
Conference_Location :
Vancouver, BC
Print_ISBN :
978-1-4244-1814-5
Electronic_ISBN :
1557-170X
DOI :
10.1109/IEMBS.2008.4650035