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Genetic subsets regression

delete1996-09-01
delete10
PRE
AI
A
Agus Sudjianto *
G
Gary S. Wasserman
H
Hinurimawan Sudarbo
DOI：10.1016/0360-8352(95)00182-4delete
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Abstract

Abstract

En 中文
Subset regression procedures have been shown to provide better overall performance than stepwise regression procedures. However, due to the combinatorial nature of evaluating each potential subset, subset regression techniques are costly to use; To resolve this difficulty, the use of a simple genetic algorithm (GA) is proposed to reduce the number of subsets which must be evaluated. Any of a number of popular criteria, including Mallows' C-p MSE, R(2), AIC, etc., can be used to drive the search strategy associated with the use of the GA. Several illustrated examples on its use are provided. Copyright (C) 1996 Elsevier Science Ltd
Keywords:
SELECTION
MODEL

Journal

Computers and Industrial Engineering cover
Computers and Industrial Engineering
IF:
6.5
Papers:
1.0W
Citations:
3.8W

Organization

No organization information available