scholarly journals Real-Valued GCS Classifier System

2007 ◽  
Vol 17 (4) ◽  
pp. 539-547 ◽  
Author(s):  
Łukasz Cielecki ◽  
Olgierd Unold
Keyword(s):  
Evolutionary Computation ◽  
Real World ◽  
Learning Classifier Systems ◽  
Classifier Systems ◽  
Context Free Grammar ◽  
New Model ◽  
Classifier System ◽  
Learning Classifier ◽  
Context Free ◽  
Real World Problems

Real-Valued GCS Classifier SystemLearning Classifier Systems (LCSs) have gained increasing interest in the genetic and evolutionary computation literature. Many real-world problems are not conveniently expressed using the ternary representation typically used by LCSs and for such problems an interval-based representation is preferable. A new model of LCSs is introduced to classify realvalued data. The approach applies the continous-valued context-free grammar-based system GCS. In order to handle data effectively, the terminal rules were replaced by the so-called environment probing rules. The rGCS model was tested on the checkerboard problem.

Approach to Clustering with Variance-Based XCS

2017 ◽  
Vol 21 (5) ◽  
pp. 885-894 ◽  
Author(s):  
Caili Zhang ◽  
Takato Tatsumi ◽  
Masaya Nakata ◽  
Keiki Takadama ◽  
◽  
...  
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Real World ◽  
Input Data ◽  
Bench Mark ◽  
Learning Classifier System ◽  
Classifier System ◽  
Learning Classifier ◽  
Real World Problems

This paper presents an approach to clustering that extends the variance-based Learning Classifier System (XCS-VR). In real world problems, the ability to combine similar rules is crucial in the knowledge discovery and data mining field. Conventionally, XCS-VR is able to acquire generalized rules, but it cannot further acquire more generalized rules from these rules. The proposed approach (called XCS-VRc) accomplishes this by integrating similar generalized rules. To validate the proposed approach, we designed a bench-mark problem to examine whether XCS-VRc can cluster both the generalized and more generalized features in the input data. The proposed XCS-VRc proved to be more efficient than XCS and the conventional XCS-VR.

scholarly journals For Real! XCS with Continuous-Valued Inputs

2003 ◽  
Vol 11 (3) ◽  
pp. 299-336 ◽  
Author(s):  
Christopher Stone ◽  
Larry Bull
Keyword(s):  
Real World ◽  
Sampling Bias ◽  
Learning Classifier Systems ◽  
Classifier Systems ◽  
Learning Classifier System ◽  
System Architectures ◽  
The Real ◽  
Learning Classifier ◽  
Real World Problem ◽  
Associated Operators

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 propose a new interval-based representation that is more straightforward than the previous ones and analyse its bias. The representations presented and their analysis are also applicable to other Learning Classifier System architectures. We discuss limitations of the real multiplexer problem, a benchmark problem used for Learning Classifier Systems that have a continuous-valued representation, and propose a new test problem, the checkerboard problem, that matches many classes of real-world problem more closely than the real multiplexer. Representations and operators are compared using both the real multiplexer and checkerboard problems and we find that representational, operator and sampling bias all affect the performance of XCS in continuous-valued environments.

Performance and Efficiency of Memetic Pittsburgh Learning Classifier Systems

2009 ◽  
Vol 17 (3) ◽  
pp. 307-342 ◽  
Author(s):  
Jaume Bacardit ◽  
Natalio Krasnogor
Keyword(s):  
Learning Classifier Systems ◽  
Classification Rules ◽  
Classifier Systems ◽  
Learning Classifier System ◽  
Classifier System ◽  
Learning Classifier ◽  
Rule Sets ◽  
Rule Set ◽  
Computational Resources ◽  
Robust To Noise

In this paper we empirically evaluate several local search (LS) mechanisms that heuristically edit classification rules and rule sets to improve their performance. Two kinds of operators are studied, (1) rule-wise operators, which edit individual rules, and (2) a rule set-wise operator, which takes the rules from N parents (N ≥ 2) to generate a new offspring, selecting the minimum subset of candidate rules that obtains maximum training accuracy. Moreover, various ways of integrating these operators within the evolutionary cycle of learning classifier systems are studied. The combinations of LS operators and policies are integrated in a Pittsburgh approach framework that we call MPLCS for memetic Pittsburgh learning classifier system. MPLCS is systematically evaluated using various metrics. Several datasets were employed with the objective of identifying which combination of operators and policies scale well, are robust to noise, generate compact solutions, and use the least amount of computational resources to solve the problems.

An Analysis of Generalization in the XCS Classifier System

1999 ◽  
Vol 7 (2) ◽  
pp. 125-149 ◽  
Author(s):  
Pier Luca Lanzi
Keyword(s):  
Learning Classifier Systems ◽  
Major Advance ◽  
Classifier Systems ◽  
Learning Mechanism ◽  
Q Learning ◽  
Classifier System ◽  
Proper Functioning ◽  
Learning Classifier ◽  
Systems Research ◽  
Learning Technique

The XCS classifier system represents a major advance in learning classifier systems research because (1) it has a sound and accurate generalization mechanism, and (2) its learning mechanism is based on Q-learning, a recognized learning technique. In taking XCS beyond its very first environments and parameter settings, we show that, in certain difficult sequential (“animat”) environments, performance is poor. We suggest that this occurs because in the chosen environments, some conditions for proper functioning of the generalization mechanism do not hold, resulting in overly general classifiers that cause reduced performance. We hypothesize that one such condition is a lack of sufficiently wide exploration of the environment during learning. We show that if XCS is forced to explore its environment more completely, performance improves dramatically. We propose a technique, based on Sutton's Dyna concept, through which wider exploration would occur naturally. Separately, we demonstrate that the compactness of the representation evolved by XCS is limited by the number of instances of each generalization actually present in the environment. The paper shows that XCS's generalization mechanism is effective, but that the conditions under which it works must be clearly understood.

ZCS Redux

2002 ◽  
Vol 10 (2) ◽  
pp. 185-205 ◽  
Author(s):  
Larry Bull ◽  
Jacob Hurst
Keyword(s):  
Genetic Algorithms ◽  
Difference Equation ◽  
Optimal Performance ◽  
Learning Classifier Systems ◽  
Rule Discovery ◽  
Classifier Systems ◽  
Learning Classifier System ◽  
Classifier System ◽  
Learning Classifier ◽  
Simple Difference

Learning classifier systems traditionally use genetic algorithms to facilitate rule discovery, where rule fitness is payoff based. Current research has shifted to the use of accuracy-based fitness. This paper re-examines the use of a particular payoff-based learning classifier system—ZCS. By using simple difference equation models of ZCS, we show that this system is capable of optimal performance subject to appropriate parameter settings. This is demonstrated for both single- and multistep tasks. Optimal performance of ZCS in well-known, multistep maze tasks is then presented to support the findings from the models.

Bounding the Effect of Noise in Multiobjective Learning Classifier Systems

2003 ◽  
Vol 11 (3) ◽  
pp. 279-298 ◽  
Author(s):  
Xavier Llorà ◽  
David E. Goldberg
Keyword(s):  
Theoretical Model ◽  
Noisy Data ◽  
Learning Classifier Systems ◽  
Data Sets ◽  
Classifier Systems ◽  
Learning Classifier System ◽  
Classifier System ◽  
Learning Classifier ◽  
Noisy Domains ◽  
The Impact

This paper analyzes the impact of using noisy data sets in Pittsburgh-style learning classifier systems. This study was done using a particular kind of learning classifier system based on multiobjective selection. Our goal was to characterize the behavior of this kind of algorithms when dealing with noisy domains. For this reason, we developed a theoretical model for predicting theminimal achievable error in noisy domains. Combining this theoretical model for crisp learners with graphical representations of the evolved hypotheses through multiobjective techniques, we are able to bound the behavior of a learning classifier system. This kind of modeling lets us identify relevant characteristics of the evolved hypotheses, such as overfitting conditions that lead to hypotheses that poorly generalize the concept to be learned.

Sign in / Sign up

Export Citation Format

Share Document