@INBOOK{gastacking-heuristic01,
  chapter = {Heuristic Search Based Stacking of Classifiers},
  pages = {54--67},
  title = {Data Mining: a Heuristic Approach},
  publisher = {Idea Group Publishing},
  year = {2001},
  editor = {Hussein A. Abbass and Ruhul Sarker and Charles Newton},
  author = {Agapito Ledezma and Ricardo Aler and Daniel Borrajo},
  abstract = {Currently, the combination of several classifiers is one of the most
	active
	
	fields within inductive learning. Examples of such techniques are
	boost-
	
	ing, bagging and stacking. From these three techniques, stacking is
	
	perhaps the least used one. One of the main reasons for this relates
	to the
	
	difficulty to define and parameterize its components: selecting which
	
	combination of base classifiers to use, and which classifiers to use
	as the
	
	meta-classifier. The approach we present in this chapter poses this
	
	problem as an optimization task, and then uses optimization techniques
	
	based on heuristic search to solve it. In particular, we apply genetic
	
	algorithms to automatically obtain the ideal combination of learning
	
	methods for the stacking system.},
  bib2html_pubtype = {Book Chapter},
  bib2html_rescat = {Ensemble of Classifiers},
  isbn = {1-930708-26-2},
  key = {conjuntos},
  owner = {ledezma},
  timestamp = {2011.11.21}
}

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