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Evolutionary Learning of Linear Trees with Embedded Feature Selection
[chapter]
2006
Lecture Notes in Computer Science
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
doi:10.1007/11785231_43
fatcat:z25wavtbofguxaftprp2mmvmtq