RT Journal Article
T1 Exploring the stacking state-space
A1 Ledezma Espino, Agapito Ismael
A1 Aler, Ricardo
A1 Borrajo Millán, Daniel
AB Nowadays, there is no doubt that machine learning techniques can be successfully applied to data mining tasks. Currently, the combination of several classifiers is one of the most active fields within inductive machine learning. Examples of such techniques are boosting, bagging and stacking. From these three techniques, stacking is perhaps the less 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 classifier to use as the meta-classifier. One could use for that purpose simple search methods (e.g. hill climbing), or more complex ones (e.g. genetic algorithms). But before search is attempted, it is important to know the properties of the search space itself. In this paper we study exhaustively the space of Stacking systems that can be built by using four base learning systems: C4.5, IB1, Naive Bayes, and PART. We have also used the Multiple Linear Response (MLR) as meta-classifier. The properties of this state-space obtained in this paper will be useful for designing new Stacking-based algorithms and tools.
PB World Scientific Publishing
SN 0218-2130
YR 2002
FD 2002
LK https://hdl.handle.net/10016/5790
UL https://hdl.handle.net/10016/5790
LA eng
DS e-Archivo
RD 2 jun. 2024