NDLI: Artificial Data Sets Based on Knowledge Generators: Analysis of Learning Algorithms Efficiency

Content Provider	IEEE Xplore Digital Library
Author	Rios-Boutin, J. Orriols-Puig, A. Garrell-Guiu, J.-M.
Copyright Year	2008
Description	Author affiliation: Grup de Recerca en Sistemes Intelligents, Univ. Ramon Llull, Barcelona (Rios-Boutin, J.; Orriols-Puig, A.; Garrell-Guiu, J.-M.)
Abstract	This paper proposes a methodology to generate artificial data sets to evaluate the behavior of machine learning techniques. The methodology relies in the definition of a domain and the generation of data sets from this domain by means of different sampling processes. Then, learners are trained with the generated data sets and the created models are compared with the original domain to evaluate the quality of the learners. In the present work, a particular implementation of this methodology is provided, which is defined to test learning techniques that use a binary rule knowledge representation. As a case study, the behavior of XCS, the most influential learning classifier system, is analyzed following the methodology.
Starting Page	873
Ending Page	878
File Size	274158
Page Count	6
File Format	PDF
ISBN	9780769533261
DOI	10.1109/HIS.2008.144
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-10
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multiplexing Training Sampling Methods Evolution (biology) Efficiency Analysis Machine learning Knowledge representation Learning Classifier Systems Sampling methods Articial Data Sets Classification algorithms Machine Learning
Content Type	Text
Resource Type	Article

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