Evolutionary Learning of Linear Trees with Embedded Feature Selection | Zendy

Marek Krętowski | Zendy; Marek Grześ | Zendy

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Evolutionary Learning of Linear Trees with Embedded Feature Selection

Author(s) -

Marek Krętowski,

Marek Grześ

Publication year - 2006

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.249

H-Index - 400

eISSN - 1611-3349

pISSN - 0302-9743

ISBN - 3-540-35748-3

DOI - 10.1007/11785231_43

Subject(s) - computer science , feature selection , artificial intelligence , selection (genetic algorithm) , feature (linguistics) , machine learning , pattern recognition (psychology) , philosophy , linguistics

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 non-terminal 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.

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