Evolving full oblique decision trees

This paper presents a novel algorithm for induction of full oblique decision trees (EFTI). Proposed algorithm is based on special, single individual evolutionary algorithm, which evolves full decision tree by modifying its structure and node coefficients during the evolution process. EFTI algorithm is particularly well suited to be used in embedded applications, because it uses much less computational resources when compared with existing full DT inference algorithms. Performance of proposed EFTI algorithm, in terms of accuracy and tree sizes of evolved decision trees, has been studied and compared with nine previously proposed decision tree building algorithms, using selected datasets from the standard UCI Machine Learning Repository database. Results of conducted experiments suggest that proposed EFTI algorithm generally generates significantly smaller decision trees than the ones produced by previously proposed algorithms, while retaining the classification accuracy.