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This application allow user to predict dissolution profile of solid dispersion systems based on algorithms like symbolic regression, deep neural networks, random forests or generalized boosted models. Those techniques can be combined to create expert system.
Application was created as a part of project K/DSC/004290 subsidy for young researchers from Polish Ministry of Higher Education.
...New Features Include:
-All the Features of the 3.7.3 Weka Package
-Multi-Threaded ensemble learning
-An enhancement on the popular RandomForest Learner based on "Dynamic Integration with Random Forests" by Tsymbal et al. 2006 and "Improving Random Forests" by Robnik-Sikonja 2004.
-More enhancements to the voting mechanisms in RandomForest
-Possibility to output Feature Weights according to the original Breiman Paper 2001
RandomForest classification implementation in Java based on Breiman's algorithm (2001). It assumes the data is in the form [ X_1, X_2, . . ., X_M, Y ] where Y \in {0, 1, . . ., C}. The user must define M, C, and m initially.