Missing Values and Learning of Fuzzy Rules

Berthold, Michael R.; Huber, Klaus-Peter

doi:10.1142/s021848859800015x

Cited by 22 publications

(15 citation statements)

References 2 publications

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“…This method does not use any sort imputation. Evolving models which iteratively use a best guest of the missing data were proposed for classification [10] and for identification of an ARX model [11]. In [4], four strategies for using the Fuzzy c-Means algorithm (FCM) in incomplete data sets are proposed.…”

Section: Introductionmentioning

confidence: 99%

A new approach to dealing with missing values in data-driven fuzzy modeling

Almeida

Kaymak

Sousa

2010

International Conference on Fuzzy Systems

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da, J. M. (2010). A new approach to dealing with missing values in data-driven fuzzy modeling. In Proceedings of the World Congress on Computational Intelligence (WCCI 2010), July 18-23, 2010, Barcelona, Spain (pp. 312-318). Piscataway: Institute of Electrical and Electronics Engineers (IEEE). DOI: 10.1109/FUZZY.2010 General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Abstract-Real word data sets often contain many missing elements. Most algorithms that automatically develop a rulebased model are not well suited to deal with incomplete data. The usual technique is to disregard the missing values or substitute them by a best guess estimate, which can bias the results. In this paper we propose a new method for estimating the parameters of a Takagi-Sugeno fuzzy model in the presence of incomplete data. We also propose an inference mechanism that can deal with the incomplete data. The presented method has the added advantage that it does not require imputation or iterative guess-estimate of the missing values. This methodology is applied to fuzzy modeling of a classification and regression problem. The performance of the obtained models are comparable with the results obtained when using a complete data set.

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Section: Introductionmentioning

confidence: 99%

A new approach to dealing with missing values in data-driven fuzzy modeling

Almeida

Kaymak

Sousa

2010

International Conference on Fuzzy Systems

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“…In Hong and Lee a general learning method was proposed for automatically deriving MFs and FRs from a set of given training examples in order to rapidly build a prototype fuzzy expert system [72]. A technique was proposed by Berthold and Huber to tolerate missing values based on a system of FRs for classification [73]. Another well thought out method called characteristic-point-based fuzzy inference system (CPFIS) was presented by Yin [74].…”

Section: Existing Literature On Fuzzy Modelingmentioning

confidence: 99%

Flow Discharge Modeling in Open Canals Using a New Fuzzy Modeling Technique (SMRGT)

Toprak¹

2009

CLEAN Soil Air Water

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The main goal of this study was to research the answer to two important questions in flow modeling; i) how to optimally design the cross-section of an open channel for a given flow, and ii) in the case of selecting the fuzzy method for modeling, how to construct the membership functions (MFs) and fuzzy rules (FRs) such that the system yields the best results. The first question is answered in order to minimize difficulties in excavation and related costs by using the appropriate flow velocity. To provide the best answer researchers use several methods. The second question is answered in order to minimize model error. For this aim, there are many algorithms proposed by researchers in the literature. In this paper, the fuzzy logic method was used for open canal flow modeling. Furthermore, a simple membership function and fuzzy rule generation technique (SMRGT) is introduced, and used for fuzzy modeling. Two fuzzy models, each for different cross-sectional shape, are presented in this study as an application of SMRGT. The comparison depends on various statistics, mean absolute relative error, and contour maps showed that the fuzzy models were successful in open channel flow modeling and SMRGT is useful for MF (membership function) and FR (fuzzy rule) generation.

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“…They are also well known for being able to deal with imprecise data. However, few analysis have been carried out considering the presence of MVs (Berthold and Huber 1998;Gabriel and Berthold 2005) for FRBCSs and usually the presence of MVs is not usually taken into account and they are usually discarded, maybe inappropriately. Incomplete data in either the training set or test set or in both sets affect the prediction accuracy of learned classifiers (Gheyas and Smith 2010).…”

Section: Introductionmentioning

confidence: 99%

Missing data imputation for fuzzy rule-based classification systems

2011

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Fuzzy rule-based classification systems (FRBCSs) are known due to their ability to treat with low quality data and obtain good results in these scenarios. However, their application in problems with missing data are uncommon while in real-life data, information is frequently incomplete in data mining, caused by the presence of missing values in attributes. Several schemes have been studied to overcome the drawbacks produced by missing values in data mining tasks; one of the most well known is based on preprocessing, formerly known as imputation. In this work, we focus on FRBCSs considering 14 different approaches to missing attribute values treatment that are presented and analyzed. The analysis involves three different methods, in which we distinguish between Mamdani and TSK models. From the obtained results, the convenience of using imputation methods for FRBCSs with missing values is stated. The analysis suggests that each type behaves differently while the use of determined missing values imputation methods could improve the accuracy obtained for these methods. Thus, the use of particular imputation methods conditioned to the type of FRBCSs is required.

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Missing Values and Learning of Fuzzy Rules

Cited by 22 publications

References 2 publications

A new approach to dealing with missing values in data-driven fuzzy modeling

A new approach to dealing with missing values in data-driven fuzzy modeling

Flow Discharge Modeling in Open Canals Using a New Fuzzy Modeling Technique (SMRGT)

Missing data imputation for fuzzy rule-based classification systems

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