RT Conference Proceedings
T1 Empirical study of a 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. The results that have been obtained in this paper will be useful for designing new Stacking-based algorithms and tools.
PB IEEE
SN 0-7695-1417-0
YR 2001
FD 2001-11
LK https://hdl.handle.net/10016/6100
UL https://hdl.handle.net/10016/6100
LA eng
NO Proceedings of: 13th International Conference on Tools with Artificial Intelligence, 7-9 Nov. 2001 Dallas
DS e-Archivo
RD 19 may. 2024