A comparison of three additive tree algorithms that rely on a least-squares loss criterion.
The performances of three additive tree algorithms which seek to minimize a least-squares loss criterion were compared. The algorithms included the penalty-function approach of De Soete (1983), the iterative projection strategy of Hubert & Arabie (1995) and the two-stage ADDTREE algorithm, (Corter, 1982; Sattath & Tversky, 1977). Model fit, comparability of structure, processing time and metric recovery were assessed. Results indicated that the iterative projection strategy consistently located the best-fitting tree, but also displayed a wider range and larger number of local optima.
Smith
T J
TJ
Department of Psychology, University of Illinois, Urbana-Champaign 61820, USA.
D000465:Algorithms; D006801:Humans; D016018:Least-Squares Analysis; D009010:Monte Carlo Method; D011594:Psychometrics