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Knowledge-based instance selection: A compromise between efficiency and versatility.
- Source :
-
Knowledge-Based Systems . Jul2013, Vol. 47, p65-76. 12p. - Publication Year :
- 2013
-
Abstract
- Abstract: Traditionally, each instance selection proposal applies the same selection criterion to any problem. However, the performance of such criteria depends on the input data and a single one is not sufficient to guarantee success over a wide range of environments. An option to adapt the selection criteria to the input data is the use of meta-learning to build knowledge-based systems capable to choose the most appropriate selection strategy among several available candidates. Nevertheless, there is not in the literature a theoretical framework that guides the design of instance selection techniques based on meta-learning. This paper presents a framework for this purpose as well as a case study in which the framework is instantiated and an experimental study is carried out to show that the meta-learning approach offers a good compromise between efficiency and versatility in instance selection. [Copyright &y& Elsevier]
- Subjects :
- *MACHINE learning
*EXPERT systems
*PROBLEM solving
*THEORY of knowledge
*EXPERIMENTS
Subjects
Details
- Language :
- English
- ISSN :
- 09507051
- Volume :
- 47
- Database :
- Academic Search Index
- Journal :
- Knowledge-Based Systems
- Publication Type :
- Academic Journal
- Accession number :
- 89271496
- Full Text :
- https://doi.org/10.1016/j.knosys.2013.04.005