| TiMBL version 4.2 TiMBL is a machine learning program implementing a family of Memory-Based Learning techniques. TiMBL stores a representation of the training set explicitly in memory (hence `Memory Based'), and classifies new cases by extrapolating from the most similar stored cases. TiMBL is being developed with a focus on classification tasks with symbolic data, large numbers of features and values, and very large case bases, as typically found in natural language processing. However, TiMBL can be applied to any machine learning or data mining task for which labeled examples with fixed numbers of features are available. The main features of the system are: - Support for symbolic, numeric and binary features. - Automatic feature weighting. Information Gain, Gain Ratio, Chi-squared, and Shared Variance weighting are provided for dealing with features of differing importance. - Stanfill & Waltz's / Cost & Salzberg's (Modified) Value Difference metric for making graded guesses of the match between two different symbolic values. - Speed up optimizations that enhance the underlying k-nearest neighbor classifier kernel: Conversion of the flat instance memory into a decision tree, and inverted indexing of the instance memory, both yielding faster classification. - Further compression and pruning of the decision tree, guided by feature information gain differences, for even larger speed-ups (the IGTREE and TRIBL learning algorithms). - Verbose output to enable the monitoring of the process of extrapolation from nearest neighbors. - A multithreaded TiMBL server that can be used as a classification agent. - Fast leave-one-out testing. Version 4.2 offers a number of new features: - Class voting weighted by distance (inverse, linear, or decayed exponentially) or by user-defined exemplar weights. - Emulation of the IB2 algorithm, an incremental editing variant of IB1 (Aha, Kibler and Albert, 1991). - Internal n-fold cross-validation testing. - Various additional verbosity options, bug-fixes and code improvements. For more information: The reference guide ("TiMBL: Tilburg Memory-Based Learner, version 4.2, Reference Guide.", Walter Daelemans, Jakub Zavrel, Ko van der Sloot, and Antal van den Bosch. ILK Technical Report 02-01) can be downloaded separately and directly from http://ilk.kub.nl/downloads/pub/papers/ilk.0201.ps | | |
