Assessing how feature selection and hyper-parameters influence optimal trees ensemble and random projection

Assessing how feature selection and hyper-parameters influence optimal trees ensemble and random projection

Our work investigates the effect of feature selection on three methods: Random Forest (Breiman 2001), Optimal Trees Ensemble (Khan et al 2016) and Random Projection (Canning and Samworth 2017) in high dimensional settings.

To this end, LASSO has been considered for selecting the most important features based on training data for dimension reduction. Additionally, the influence of various hyper-parameters regulating the three methods has also been assessed. Analysis on several benchmark datasets is given to illustrate the phenomena. The results reveal that feature selection improves the predictive performance of the Random Forest and Random Projection methods in addition to reducing the computational burden. The performance of Optimal Trees Ensemble is less influenced by feature selection.

Speaker

Nosheen Faiz (PhD Student), University of Essex

How to attend

If not a member of the Dept. Mathematical Science at the University of Essex, you can register your interest in attending the seminar and request the Zoom’s meeting password by emailing Dr Osama Mahmoud (o.mahmoud@essex.ac.uk).