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Asian Journal of Control, Vol. 6, No. 3, pp. 439-446, September 2004
439
－Brief Paper－
RANDOM APPROXIMATED GREEDY SEARCH
FOR FEATURE SUBSET SELECTION
Feng Gao and Yu-Chi Ho
ABSTRACT
We propose a sequential approach called Random Approximated
Greedy Search (RAGS) in this paper and apply it to the feature subset
selection for regression. It is an extension of GRASP/Super-heuristics
approach to complex stochastic combinatorial optimization problems, where
performance estimation is very expensive. The key points of RAGS are from
the methodology of Ordinal Optimization (OO). We soften the goal and
define success as good enough but not necessarily optimal. In this way, we
use more crude estimation model, and treat the performance estimation error
as randomness, so it can provide random perturbations mandated by the
GRASP/Super-heuristics approach directly and save a lot of computation
effort at the same time. By the multiple independent running of RAGS, we
show that we obtain better solutions than standard greedy search under the
comparable computation effort.
KeyWords: Feature subset selection, ordinal optimization, greedy search,
stochastic combinatorial optimization.