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This project provides a Java implementation of random forests. Random forests use training sets to build decision trees. Given an input (e.g. a person with age, gender, medical background, symptoms) the result (e.g. a disease) of which is unknown, random forests are able to predict the corresponding result.

This module provides two packages, 'trees' and 'forests', which provide algorithms to compute edit distances on trees and forests (that is, unordered or ordered lists of trees) respectively. The edit distance is computed according to the tree edit distance algorithm of Zhang and Shasha (1989). The basic tree data structure is defined by the Tree interface in the trees module. Please refer to the javadoc for more detailed information.

DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples. Comprehensive experiments have demonstrated that this technique is consistently more accurate than the base classifier, Bagging and Random Forests. Decorate also obtains higher accuracy than Boosting on small training sets, and achieves comparable performance on larger training sets. For more details see: P. Melville, R. J. Mooney: Constructing Diverse Classifier Ensembles Using Artificial Training Examples. In: Eighteenth International Joint Conference on Artificial Intelligence, 505-510, 2003; P. Melville, R. J. Mooney (2004). Creating Diversity in Ensembles Using Artificial Data. Information Fusion: Special Issue on Diversity in Multiclassifier Systems.