A comparison of three additive tree algorithms that rely on a least‐squares loss criterion | Zendy

Smith Thomas J. | Zendy

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A comparison of three additive tree algorithms that rely on a least‐squares loss criterion

Author(s) -

Smith Thomas J.

Publication year - 1998

Publication title -

british journal of mathematical and statistical psychology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.157

H-Index - 51

eISSN - 2044-8317

pISSN - 0007-1102

DOI - 10.1111/j.2044-8317.1998.tb00681.x

Subject(s) - comparability , algorithm , mathematics , metric (unit) , tree (set theory) , least squares function approximation , range (aeronautics) , projection (relational algebra) , mathematical optimization , computer science , statistics , combinatorics , operations management , materials science , estimator , economics , composite material

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.

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