A system for hardware aided decision tree ensemble evolution

In this paper a system for hardware-aided induction of decision tree ensembles using the evolutionary approach (Decision Tree Ensemble Evolution co-Processor-DTEEP) is proposed. DTEEP is used for hardware acceleration of the fitness evaluation, since it is shown that most of the ensemble inference time is spent on this task. The DTEEP co-processor can significantly improve execution time of an algorithm for a full tree evolutionary induction of the decision tree ensembles (An Evolutionary Ensemble of Full Trees Induction-EEFTI) by accelerating the fitness evaluation task. Comparing the HW/SW EEFTI implementation with the pure software implementation suggests that the proposed HW/SW architecture offers substantial speedups for all the tests performed on the selected UCI (University of California, Irvine) machine learning repository datasets.