Rule weight specification in fuzzy rule-based classification systems

Ishibuchi, Hisao; Yamamoto, Takashi

doi:10.1109/tfuzz.2004.841738

Cited by 423 publications

(232 citation statements)

References 15 publications

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“…Many approaches have been proposed for generating and learning fuzzy if-then rules from numerical data for classification problems [28,29]. FRBS are used by Chang and Liu [30] for stock price prediction, while Ishibuchi and Yamamoto [31] show how the rule weight of each fuzzy rule can be specified in FRBS in the case of multiclass pattern classification problems. Of interest to this paper are data-driven FRBS for handling classification tasks.…”

Section: Introductionmentioning

confidence: 99%

A quantifier-based fuzzy classification system for breast cancer patients

Soria

Garibaldi

Green

et al. 2013

Artificial Intelligence in Medicine

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A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. Consensus between unsupervised classification algorithms has been successfully used to categorise patients into these specific groups, but often at the expenses of not classifying the whole set. It is known that fuzzy methodologies can provide linguistic based classification rules. The objective of this study was to investigate the use of fuzzy methodologies to create an easy to interpret set of classification rules, capable of placing the large majority of patients into one of the specified groups. Methods and materials:In this paper, we extend a data-driven fuzzy rule-based system for classification purposes (called 'fuzzy quantification subsethood-based algorithm') and combine it with a novel class assignment procedure. The whole approach is then applied to a well characterised breast cancer dataset consisting of ten protein markers for over 1,000 patients to refine previously identified groups and to present clinicians with a linguistic ruleset. A range of statistical approaches were used to compare the obtained classes to previously obtained groupings and to assess the proportion of unclassified patients.Results: A rule set was obtained from the algorithm which features one classification rule per class, using labels of High, Low or Omit for each biomarker, to determine the most appropriate class for each patient. When applied to the whole set of patients, the distribution of the obtained classes had an agreement of 0.9 when assessed using Kendall's Tau with the original reference class distribution. In doing so, only 38 patients out of 1,073 remain unclassified, representing a more clinically usable class assignment algorithm. Conclusion:The fuzzy algorithm provides a simple to interpret, linguistic rule set which classifies over 95% of breast cancer patients into one of seven clinical groups.

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Section: Introductionmentioning

confidence: 99%

A quantifier-based fuzzy classification system for breast cancer patients

Soria

Garibaldi

Green

et al. 2013

Artificial Intelligence in Medicine

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“…Ishibuchi and Nakashima (2001); Ishibuchi and Yamamoto (2005) argued that weighing rules according to (4) allows for modeling more flexible decision boundaries and thereby improves classification accuracy. The certainty factor (5) is the m-estimate for m = 2 (Press et al, 1992).…”

Section: Classifier Outputmentioning

confidence: 99%

“…Additionally, we also included the C4.5 decision tree learner (Quinlan, 1993) as a wellknown benchmark classifier and, moreover, added two fuzzy rule-based classifiers from the KEEL suite (Alcalá-Fernandez et al, 2008): The CHI algorithm is based on Chi et al (1995Chi et al ( , 1996 and uses rule weighing as proposed by Ishibuchi and Yamamoto (2005). 6 The SLAVE algorithm makes use of genetic algorithms to learn a fuzzy classifier Perez, 1999, 2001).…”

Section: Classification Accuracymentioning

confidence: 99%

“…To a large extent, the popularity of rule-based models can be attributed to their comprehensibility, a distinguishing feature and key advantage in comparison to many other (black-box) classification models. Despite the existence of many sound algorithms for rule induction, the field still enjoys great popularity and, as shown by recent publications (Ishibuchi and Yamamoto, 2005;Cloete and Van Zyl, 2006;Juang et al, 2007;Fernández et al, 2007), offers scope for further improvements.…”

Section: Introductionmentioning

confidence: 99%

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FURIA: an algorithm for unordered fuzzy rule induction

Hühn

Hüllermeier

2009

Data Min Knowl Disc

374

223

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This paper introduces a novel fuzzy rule-based classification method called FURIA, which is short for Fuzzy Unordered Rule Induction Algorithm. FURIA extends the well-known RIPPER algorithm, a state-of-the-art rule learner, while preserving its advantages, such as simple and comprehensible rule sets. In addition, it includes a number of modifications and extensions. In particular, FURIA learns fuzzy rules instead of conventional rules and unordered rule sets instead of rule lists. Moreover, to deal with uncovered examples, it makes use of an efficient rule stretching method. Experimental results show that FURIA significantly outperforms the original RIPPER, as well as other classifiers such as C4.5, in terms of classification accuracy.

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“…Several methods have been examined to determine the rule weight of each fuzzy rule in the literature [25] where good results are obtained from the following specification:…”

Section: Fuzzy Rule-based Classifiersmentioning

confidence: 99%

Fuzzy Ensemble Design through Multi-Objective Fuzzy Rule Selection

Ishibuchi

Nojima

Multi-Objective Machine Learning

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Summary.The main advantage of evolutionary multi-objective optimization (EMO) over classical approaches is that a variety of non-dominated solutions with a wide range of objective values can be simultaneously obtained by a single run of an EMO algorithm. In this chapter, we show how this advantage can be utilized in the design of fuzzy ensemble classifiers. First we explain three objectives in multi-objective formulations of fuzzy rule selection. One is accuracy maximization and the others are complexity minimization. Next we demonstrate that a number of non-dominated rule sets (i.e., fuzzy classifiers) are obtained along the accuracy-complexity tradeoff surface from multi-objective fuzzy rule selection problems. Then we examine the effect of combining multiple non-dominated fuzzy classifiers into a single ensemble classifier. Experimental results clearly show that the combination into ensemble classifiers improves the classification ability of individual fuzzy classifiers for some data sets. IntroductionA promising approach to the design of reliable classifiers is to combine multiple classifiers into a single one [2,10]. Several methods have been proposed for generating multiple classifiers such as bagging [3] and boosting [12]. The point in classifier aggregation is to generate an ensemble of classifiers with high diversity. Ideally the classification errors by each classifier should be uncorrelated. In this chapter, we examine the use of evolutionary multiobjective optimization (EMO) algorithms for generating an ensemble of fuzzy rule-based classifiers with high diversity.The main advantage of evolutionary multiobjective optimization (EMO) over classical approaches is that many non-dominated solutions can be simultaneously obtained by a single run of an EMO algorithm. When an EMO algorithm is used in the design of an ensemble classifier, a number of nondominated classifiers with high diversity can be obtained by its single run. This advantage has already been utilized in some studies on the design of neural

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Rule weight specification in fuzzy rule-based classification systems

Cited by 423 publications

References 15 publications

A quantifier-based fuzzy classification system for breast cancer patients

A quantifier-based fuzzy classification system for breast cancer patients

FURIA: an algorithm for unordered fuzzy rule induction

Fuzzy Ensemble Design through Multi-Objective Fuzzy Rule Selection

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