Implementation of the Optimal Subforest algorithm "OptimizedForest", which was published in:
Md Nasim Adnan and Md Zahidul Islam: Optimizing the number of trees in a decision forest to discover a subforest with high ensemble accuracy using a genetic algorithm In: Knowledge-Based Systems Vol 110, 2016
This algorithm builds a decision forest and then works out an optimal subforest via Genetic Algorithm.
@article{adnan2016optimizing,
title={Optimizing the number of trees in a decision forest to discover a subforest with high ensemble accuracy using a genetic algorithm},
author={Adnan, Md Nasim and Islam, Md Zahidul},
journal={Knowledge-Based Systems},
volume={110},
pages={86--97},
year={2016},
publisher={Elsevier}
}
Either download OptimizedForest from the Weka package manager, or download the latest release from the "Releases" section on the sidebar of Github.
Set up a project in your IDE of choice, including weka.jar as a compile-time library.
-S <num>;
Seed for random number generator. (default 1)
-I <num>
Number of iterations for genetic algorithm. (default 20)
-P <num>
Initial population size for genetic algorithm. (default 20)
-C < RandomForest | Bagging >
Decision forest building method. (Default = RandomForest)