This package provides machine learning algorithms optimized for large text categorization tasks and is able to combine several text categorization solutions. The advantages of this package compared to existing approaches are: 1) its speed, 2) it is able to work with a large number of categorization problems and, 3) it provides the ability to compare several text categorization tools based on meta-learning.
The MTI ML package also contains a complete tutorial environment with training and test data collections, a sample configuration file, and a benchmark file with the gold standard annotations for the data sets to evaluate the results. This provides the ability to recreate the entire MTI ML environment from training the machine learning algorithms to finally evaluating the results.
A. Jimeno Yepes, J.G. Mork, D. Demner Fushman, and A.R. Aronson, 2011. Automatic algorithm selection for MeSH Heading indexing based on meta-learning. International Symposium on Languages in Biology and Medicine, Singapore, December, 2011