Comparison of Heuristic Criteria for Fuzzy Rule Selection in Classification Problems

Ishibuchi, Hisao; Yamamoto, Takashi

doi:10.1023/b:fodm.0000022041.98349.12

Cited by 52 publications

(33 citation statements)

References 26 publications

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“…Several type-1 fuzzy classification systems have been reported in the literature such as [20], [21], [22], [23], [24], [25], [26], [27], [28] and [29]. However, in the vast majority of these papers, the data was quite easy to partition, and if an input pattern does not match any of the decision areas previously labeled, the input is discharged.…”

Section: B a Brief Overview On Fuzzy Logic Classification Systemsmentioning

confidence: 99%

An interval type-2 Fuzzy Logic based system for model generation and summarization of arbitrage opportunities in stock markets

Bernardo

Hagras

Tsang

2012

2012 12th UK Workshop on Computational Intelligence (UKCI)

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Abstract-Today stock market exchange and finance are centers of attention all over the world. In finance, arbitrage is the practice of taking advantage of a price misalignment between two or more stock markets where profit can be earned by striking a combination of matching deals that capitalize upon the misalignment. If one strikes when misalignment has been observed, such deals are practically risk-free. However, when risk-free profit is around, everyone would compete to take advantage of it. Therefore, the question is whether arbitrage opportunities can be predicted; after all, misalignment does not happen instantaneously. Furthermore, financial operators do not like black boxes in forecasting. In this paper, we will present a type-2 Fuzzy Logic System (FLS) for the modeling and prediction of financial applications. The proposed system is capable of generating summarized models from pre-specified number of linguistic rules, which enables the user to understand the generated models for arbitrage opportunities prediction. The system is able to use this summarized model for the prediction of arbitrage opportunities in stock markets. We have performed several experiments based on the arbitrage data which is used in stock markets to spot ahead of time arbitrage opportunities. The proposed type-2 FLS has outperformed the Evolving Decision Rule (EDR) procedure (which is based on Genetic Programming (GP) and decision trees). Like GP, the type-2 FLS is capable of providing a white box model which could be easily understood and analyzed by the lay user.

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Section: B a Brief Overview On Fuzzy Logic Classification Systemsmentioning

confidence: 99%

An interval type-2 Fuzzy Logic based system for model generation and summarization of arbitrage opportunities in stock markets

Bernardo

Hagras

Tsang

2012

2012 12th UK Workshop on Computational Intelligence (UKCI)

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“…For example, for a data set having n input attributes, K n fuzzy rules might be generated. An approach for handling this problem is employing some rule evaluation criteria to select a small subset of rules among all candidates [32].…”

Section: General Design Of Fuzzy-rule-based Classification Systemsmentioning

confidence: 99%

“…SLAVE uses a GA to select the rule that best represents the system. Ishibuchi and Yamamoto [32] have also utilized a GBML algorithm to select a subset of fuzzy rules incorporating the combinatorial effect of rules. However, in these approaches, the number of possible rules increases exponentially with the problem dimension.…”

Section: Introductionmentioning

confidence: 99%

SGERD: A Steady-State Genetic Algorithm for Extracting Fuzzy Classification Rules From Data

Mansoori

Zolghadri

Katebi

2008

IEEE Trans. Fuzzy Syst.

144

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Abstract-This paper considers the automatic design of fuzzyrule-based classification systems from labeled data. The performance of classifiers and the interpretability of generated rules are of major importance in these systems. In past research, some genetic-based algorithms have been used for the rule learning process. These genetic fuzzy systems have utilized different approaches to encode rules. In this paper, we have proposed a novel steadystate genetic algorithm to extract a compact set of good fuzzy rules from numerical data (SGERD). The selection mechanism of this algorithm is nonrandom, and only the best individuals can survive. Our approach is very simple and fast, and can be applied to high-dimensional problems with numerical attributes. To select the rules having high generalization capabilities, our algorithm makes use of some rule-and data-dependent parameters. We have also proposed an enhancing function that modifies the rule evaluation measures in order to assess the candidate rules more effectively before their selection. Experiments on some well-known data sets are performed to show the performance of SGERD.Index Terms-Data mining, fuzzy-rule-based classification system, fuzzy rule learning, steady-state genetic algorithm.

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“…12,13,14,15,16,17,18,19 ), we wanted to make the different versions publicly available, a task that required translating the original code of each recognized version and the code of NSLV from C++ to Java, and also adding the resulting implementations to the KEEL platform 20 .…”

Section: Introductionmentioning

confidence: 99%

Overview of the SLAVE learning algorithm: A review of its evolution and prospects

García¹,

González²,

Pérez³

2014

IJCIS

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Inductive learning has been-and still is-one of the most important methods that can be applied in classification problems. Knowledge is usually represented using rules that establish relationships between the problem variables. SLAVE (Structural Learning Algorithm in a Vague Environment) was one of the first fuzzy-rule learning algorithms, and since its first implementation in 1994 it has been frequently used to benchmark new algorithms. Over time, the algorithm has undergone several modifications, and identifying the different versions developed is not an easy task. In this work we present a study of the evolution of the SLAVE algorithm from 1996 to date, marking the most important landmarks as definitive versions. In order to add these final versions to the KEEL platform, Java implementations have been developed. Finally, we describe the parameters used and the results obtained in the experimental study.

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Comparison of Heuristic Criteria for Fuzzy Rule Selection in Classification Problems

Cited by 52 publications

References 26 publications

An interval type-2 Fuzzy Logic based system for model generation and summarization of arbitrage opportunities in stock markets

An interval type-2 Fuzzy Logic based system for model generation and summarization of arbitrage opportunities in stock markets

SGERD: A Steady-State Genetic Algorithm for Extracting Fuzzy Classification Rules From Data

Overview of the SLAVE learning algorithm: A review of its evolution and prospects

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