Evolutionary Learning of Linear Trees with Embedded Feature Selection

In the paper a new evolutionary algorithm for global induction of linear trees is presented. The learning process consists of searching for both a decision tree structure and hyper-plane weights in all nonterminal nodes. Specialized genetic operators are developed and applied according to the node quality and location. Feature selection aimed at simplification of the splitting hyper-planes is embedded into the algorithm and results in elimination of noisy and redundant features. The proposed approach is verified on both artificial and real-life data and the obtained results are promising