NDLI: An iterative strategy for feature construction on a fuzzy rule-based learning algorithm

Content Provider	IEEE Xplore Digital Library
Author	Garcia, D. Gonzalez, A. Perez, R.
Copyright Year	2011
Description	Author affiliation: Departamento de Ciencias de la Computatión e LA., CITIC-UGR University of Granada (Spain) (Garcia, D.; Gonzalez, A.; Perez, R.)
Abstract	This paper presents a proposal for using feature construction in a fuzzy rule-based learning algorithm as a method to avoid working with a fixed set of features to describe a particular problem. The main purpose is to increase the amount of information extracted from initial variables to construct a model that has better prediction capability. This approach iteratively looks for the function that obtains the best adaptation level to the examples covered by a rule. If exists, this function is added both to the antecedent of the rule and to a specific structure called catalog of functions in order to be considered by the learning algorithm. For that, a model of rule is used in order to represent this kind of knowledge in combination with the catalog, which helps us to manage the functions that have ever been considered during the learning process. Finally, a comparative study of the results obtained with this approach is presented.
Starting Page	1235
Ending Page	1240
File Size	320361
Page Count	6
File Format	PDF
ISBN	9781457716768
ISSN	21647151
e-ISBN	9781457716768
DOI	10.1109/ISDA.2011.6121828
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-22
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Databases Input variables Genetic Fuzzy Systems Classification Feature Construction Iterative Learning Approach Genetics Catalogs Pragmatics Genetic algorithms
Content Type	Text
Resource Type	Article

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