- author = "James G. Shanahan and James F. Baldwin and Trevor P. Martin",
- title = "Constructive Induction of Fuzzy Cartesian Granule Feature Models using Genetic Programming with Applications",
- booktitle = "Proceedings of the Congress on Evolutionary Computation",
- year = "1999",
- editor = "Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala",
- volume = "1",
- pages = "218--226",
- address = "Mayflower Hotel, Washington D.C., USA",
- publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA",
- month = "6-9 " # jul,
- organisation = "Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE",
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, learning, Cartesian granule fuzzy sets, G-DACG, Genetic Discovery of Additive Cartesian Granule feature models , additive Cartesian granule feature models, constituent input features, constructive induction algorithm, exponential search problem, fitness function, fuzzy Cartesian granule feature models, linguistic partitioning, optimisation capabilities, prediction problems, real world classification problems, rule based models, semantic separation, computational linguistics, fuzzy set theory, learning by example, pattern classification",
-
DOI = "
doi:10.1109/CEC.1999.781929",
- ISBN = "0-7803-5536-9 (softbound)",
- ISBN = "0-7803-5537-7 (Microfiche)",
- abstract = "Cartesian granule features are derived features that are formed over the cross product of words that linguistically partition the universes of the constituent input features. Both classification and prediction problems can be modelled quite naturally in terms of Cartesian granule features incorporated into rule based models. The induction of Cartesian granule feature model involves discovering which input features should be combined to form Cartesian granule features in order to model a domain effectively; an exponential search problem. We present the G-DACG (Genetic Discovery of Additive Cartesian Granule feature models) constructive induction algorithm as a means of automatically identifying additive Cartesian granule feature models from example data. G-DACG combines the powerful optimisation capabilities of genetic programming with a rather novel and cheap fitness function which relies on the semantic separation of learnt concepts expressed in terms of Cartesian granule fuzzy sets. G-DACG is illustrated on a variety of artificial and real world classification problems",
-
notes = "CEC-99 - A joint meeting of the IEEE, Evolutionary
Programming Society, Galesia, and the IEE.
Library of Congress Number = 99-61143",
