title: Applicability domain based on ensemble learning in classification and regression analyses.
abstract: We discuss applicability domains (ADs) based on ensemble learning in classification and regression analyses. In regression analysis, the AD can be appropriately set, although attention needs to be paid to the bias of the predicted values. However, because the AD set in classification analysis is too wide, we propose an AD based on ensemble learning and data density. First, we set a threshold for data density below which the prediction result of new data is not reliable. Then, only for new data with a data density higher than the threshold, we consider the reliability of the prediction result based on ensemble learning. By analyzing data from numerical simulations and quantitative structural relationships, we validate our discussion of ADs in classification and regression analyses and confirm that appropriate ADs can be set using the proposed method.
authors.affiliation: Department of Chemical Systems Engineering, The University of Tokyo , 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.
authors.forename: Hiromasa
authors.initials: H
authors.lastname: Kaneko
authors.forename: Kimito
authors.initials: K
authors.lastname: Funatsu
mesh_terms: D007858:Learning; D008962:Models, Theoretical; D012044:Regression Analysis