For Real! XCS with Continuous-Valued Inputs

Stone, Christopher; Bull, Larry

doi:10.1162/106365603322365315

Cited by 124 publications

(62 citation statements)

References 9 publications

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“…That allows us to introduce grammar classification to a new set of problems. When tested with the common checkerboard benchmark, the rGCS evolves a perfect grammar using a similar number of learning cycles as the XCSR develops the perfect set of classifiers (approximately 20,000 (Stone and Bull, 2003)). However, rGCS knowledge representation, using grammar rules, is far easier to interpret by a human and may be processed easily.…”

Section: Discussionmentioning

confidence: 99%

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Real-Valued GCS Classifier System

Cielecki¹,

Unold²

2007

International Journal of Applied Mathematics and Computer Science

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Learning 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.

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Section: Discussionmentioning

confidence: 99%

“…The checkerboard problem was proposed as a benchmark in (Stone and Bull, 2003). It divides the n-dimensional space into hypercubes of two colors (i.e.…”

Section: Checkerboard Problemmentioning

confidence: 99%

Real-Valued GCS Classifier System

Cielecki¹,

Unold²

2007

International Journal of Applied Mathematics and Computer Science

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“…These parameters are encoded into a single interval predicate [∆t 1 , ∆t 2 ] using an 8-bit binary encoding. This technique is a convenient means of using the same interval representation for integers that we have previously used throughout for real numbers [19]. When using a one of m binary encoding for numbers, the encoding for integers differs from that of real numbers only in the size of the alphabet used and the consequent size of the genotypic search space.…”

Section: Representationmentioning

confidence: 99%

Foreign Exchange Trading Using a Learning Classifier System

Stone

Bull

2008

Studies in Computational Intelligence

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Abstract. We apply a simple Learning Classifier System that has previously been shown to perform well on a number of difficult continuousvalued test problems to a foreign exchange trading problem. The performance of the Learning Classifier System is compared to that of a Genetic Programming approach from the literature. The simple Learning Classifier System is able to achieve a positive excess return in simulated trading, but results are not yet fully competitive because the Learning Classifier System trades too frequently. However, the Learning Classifier System approach shows potential because returns are obtained with no offline training and the technique is inherently adaptive, unlike many of the machine learning methods currently employed for financial trading.

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“…The original alphabet was ternary (the third state being the "don't care"). This has been extended to real [26,32], or fuzzy [4,7] environments, including the proposal for variable alphabets for different variables [17,18]. These extensions allow for different types of data to be represented covering variables that are qualitative, quantitative (discrete and continuous) as well as linguistically fuzzified (with hedges).…”

Section: ) Representationmentioning

confidence: 99%

A Fuzzy-XCS Classifier System with Linguistic Hedges

Marín-Blázquez

Shen²

2008

IJCIR

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Abstract:Many real-world problems require the development and application of algorithms that automatically generate human interpretable knowledge from historical data. Most existing algorithms for rule induction from imprecise data have followed the precise approach, where definitions of the fuzzy sets that are intended to capture certain vague concepts are allowed to be modified such that they fit the data. These approaches typically destroy the original semantics or meaning of the given fuzzy sets, which often leads to loss of transparency in the resulting model or models. In order to overcome this fundamental limitation, a descriptive approach has been proposed in which human defined fuzzy sets are not allowed to be modified. However, as the fuzzy set definitions cannot be modified, and only a small number of them are normally available, only a limited number of possible rules are derivable. Such rules are not very flexible and in many cases, will not necessarily fit the data well. To address this important issue, at least partially, linguistic hedges have been introduced to provide a more adaptable means of learning from data, thereby offering more flexibility in domain knowledge representation and extraction. Following this approach, this paper presents a novel rule induction mechanism which extends a classifier system (XCS) by employing linguistic hedges. The resultant fuzzy XCS classifier with linguistic hedges is evaluated against a real-world forensic glass classification problem. The results demonstrate that the inclusion of hedges to support finer granularity in linguistic fuzzy modelling improves the accuracy of the resulting classifiers, whilst simultaneously preserving the interpretability of the learned models. This approach not only offers the user rules to decide on classes, but also rules to decide which classes to discard. It also inherits from XCS, the ability to deal with data that involves imbalanced classes.

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For Real! XCS with Continuous-Valued Inputs

Cited by 124 publications

References 9 publications

Real-Valued GCS Classifier System

Real-Valued GCS Classifier System

Foreign Exchange Trading Using a Learning Classifier System

A Fuzzy-XCS Classifier System with Linguistic Hedges

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