Paper Info
Title
System Identification of Fuzzy Cartesian Granules Feature Models Using Genetic Programming
Abstract
Abstract– A Cartesian granule feature is a multidimensionalfeature formed over the cross product of words drawn,from the linguistic partitions of the constituent input features. Systems can be quite naturally described in terms of Cartesian granule features incorporated into additive models (if-then-rules with weighted antecedents) where each Cartesian granule feature focuses on modelling the interactions of a subset of input variables. This can often lead to models that reduce if not eliminate decomposition error, while enhancing the model’s generalisation powers and transparency. Within a machine learning context the system identification of good, parsimonious additive Cartesian granule feature models,is an exponential search problem. In this paper we present the G_DACG 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 concepts expressed in terms of Cartesian granule fuzzy sets in identifying these additive models. G_DACG helps avoid many,of the problems,of traditional approaches,to system identification that arise from feature selection and feature abstraction such as local minima. G_DACG has been applied in the system identification of additive Cartesian granule feature models,on a variety of artificial and real world problems. Here we present a sample of those results including those for the benchmark,Pima Diabetes problem. A classificationaccuracy,of 79.7% was achieved on this dataset outperforming previous bests of 78% (generally from black box modelling approaches such neural nets and oblique decision trees).
Year
DOI
Venue
1997
10.1007/BFb0095073
Fuzzy Logic in Artificial Intelligence (IJCAI Workshop)
Keywords
Field
DocType
genetic programming,fuzzy cartesian granules feature,system identification,feature selection,machine learning,fuzzy set,local minima,neural net,additive model,decision tree,fitness function
Feature selection,Computer science,Algorithm,Genetic programming,Fuzzy set,Fitness function,Artificial intelligence,Artificial neural network,System identification,Genetic algorithm,Machine learning,Cartesian coordinate system
Conference
ISBN
Citations 
PageRank 
3-540-66374-6
2
0.40
References 
Authors
19
3
Name
Order
Citations
PageRank
James F. Baldwin1374.45
Trevor P. Martin213426.98
James G. Shanahan345257.60