Representational Difficulties with Classifier Systems
نویسندگان
چکیده
Classifier systems are currently in vogue as a way of using genetic algorithms to demonstrate machine learning. However, there are a number of difficulties with the formalization that can influence how knowledge is represented and the rate at which the system can learn. Some of the problems are inherent in classifier systems, and one must learn to cope with them, while others are pitfalls waiting to catch the unsuspecting implementor. This paper identifies some of these difficulties, suggesting directions for the further evolution of classifier systems.
منابع مشابه
VCS: Variable Classifier Systems
Classifier systems (CS) have proven to be useful tools for the study of genetic algorithm based learning. Unfortunately, there are a number of difficulties with the formalization that limit the representational capabilities and, hence, its problem solving abilities and the speed at which it can learn. This paper introduces VCS Variable Classifier Systems that augment the traditional CS with the...
متن کاملFor Real! XCS with Continuous-Valued Inputs
Many real-world problems are not conveniently expressed using the ternary representation typically used by Learning Classifier Systems and for such problems an interval-based representation is preferable. We analyse two interval-based representations recently proposed for XCS, together with their associated operators and find evidence of considerable representational and operator bias. We propo...
متن کاملA NEURO-FUZZY GRAPHIC OBJECT CLASSIFIER WITH MODIFIED DISTANCE MEASURE ESTIMATOR
The paper analyses issues leading to errors in graphic object classifiers. Thedistance measures suggested in literature and used as a basis in traditional, fuzzy, andNeuro-Fuzzy classifiers are found to be not suitable for classification of non-stylized orfuzzy objects in which the features of classes are much more difficult to recognize becauseof significant uncertainties in their location and...
متن کاملNEW CRITERIA FOR RULE SELECTION IN FUZZY LEARNING CLASSIFIER SYSTEMS
Designing an effective criterion for selecting the best rule is a major problem in theprocess of implementing Fuzzy Learning Classifier (FLC) systems. Conventionally confidenceand support or combined measures of these are used as criteria for fuzzy rule evaluation. In thispaper new entities namely precision and recall from the field of Information Retrieval (IR)systems is adapted as alternative...
متن کاملOn the use of Heronian means in a similarity classifier
This paper introduces new similarity classifiers using the Heronian mean, and the generalized Heronian mean operators. We examine the use of these operators at the aggregation step within the similarity classifier. The similarity classifier was earlier studied with other operators, in particular with an arithmetic mean, generalized mean, OWA operators, and many more. The two classifiers here ar...
متن کامل