Genetic subsets regression

doi:10.1016/0360-8352(95)00182-4

1996-09-01

PRE

Agus Sudjianto *

Gary S. Wasserman

Hinurimawan Sudarbo

DOI：10.1016/0360-8352(95)00182-4

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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

Keyword:

SELECTION

MODEL

期刊

Computers and Industrial Engineering

IF:

6.5

论文数:

1.0W

被引数:

3.8W

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