Global feature subset selection on high-dimensional datasets using re-ranking-based EDAs

Authors:
Pablo Bermejo;Luis De La Ossa;Jose M. Puerta
Affiliations:
Edificio I³A, Castilla-La Mancha University, Albacete, Spain;Edificio I³A, Castilla-La Mancha University, Albacete, Spain;Edificio I³A, Castilla-La Mancha University, Albacete, Spain
Venue:
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
Year:
2011

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Abstract

The relatively recent appearance of high-dimensional databases has made traditional search algorithms too expensive in terms of time and memory resources. Thus, several modifications or enhancements to local search algorithms can be found in the literature to deal with this problem. However, nondeterministic global search, which is expected to perform better than local, still lacks appropriate adaptations or new developments for high-dimensional databases. We present a new non-deterministic iterative method which performs a global search and can easily handle datasets with high cardinality and, furthermore, it outperforms a wide variety of local search algorithms.