The concepts of robust classification and intelligently controlling the search process of genetic algorithm (GA) are introduced and integrated with a conventional genetic classifier for development of a new version of it, which is called Intelligent and Robust GA-classifier (IRGA-classifier). It can efficiently approximate the decision hyperplanes in the feature space. It is shown experime...

Due to their structural simplicity and superior generalization capability, Extended Classifier Systems (XCSs) are gaining popularity within the Artificial Intelligence community. In this study an improved XCS with continuous actions is introduced for function approximation purposes. The proposed XCSF uses “prediction zones,” rather than distinct “prediction values,” to enable multi-member match...

Extended Classifier Systems, or XCS, have been shown to be successful at developing accurate, complete and compact mappings of a problem’s payoff landscape. However, the experimental results presented in the literature frequently utilize population sizes significantly larger than the size of the search space. This resource requirement may limit the range of problem/hardware combinations to whic...

s of Recent Technical Reports INTERACTIVE CLASSIFICATION A Technique for the Aquisition and Maintenance of Knowledge Bases, Tim Flnln and David Silverman, MS-CIS-84-17. The practical application of flame-based knowledge-based systems, such as in expert systems, requires the maintenance of potentially very large amounts of declarative knowledge stored in their knowledge bases (KBs). As a KB grow...

Learning classifier system is a machine learning technique which combines genetic algorithm with the power of the reinforcement learning paradigm. This rule based system has been inspired by the general principle of Darwinian evolution and cognitive learning. XCS, eXtended Classifier System, is currently considered as state-of-the-art learning classifier systems due to its effectiveness in data...

Naive Credal Classifier, which is an imprecise-probability counterpart of Naive Bayes, is rigorously extended to a very general and flexible treatment of incomplete data, yielding a new classifier called Naive Credal Classifier 2 (NCC2). The new classifier delivers classifications that are robust to the presence of small sample sizes and missing values. In particular, some empirical evaluations...