A survey of defuzzification strategies

Roychowdhury, Shounak; Pedrycz, Witold

doi:10.1002/int.1030

Cited by 138 publications

(63 citation statements)

References 14 publications

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“…Several defuzzification methods for non-nominally scaled data have been proposed in published literature [12,13]. However, in remote sensing, crisp classification results are nominally scaled [14].…”

Section: Defuzzification Of Fuzzy Classification Resultsmentioning

confidence: 99%

Defuzzification Strategies for Fuzzy Classifications of Remote Sensing Data

Hofmann

2016

Remote Sensing

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Abstract:The classes in fuzzy classification schemes are defined as fuzzy sets, partitioning the feature space through fuzzy rules, defined by fuzzy membership functions. Applying fuzzy classification schemes in remote sensing allows each pixel or segment to be an incomplete member of more than one class simultaneously, i.e., one that does not fully meet all of the classification criteria for any one of the classes and is member of more than one class simultaneously. This can lead to fuzzy, ambiguous and uncertain class assignation, which is unacceptable for many applications, indicating the need for a reliable defuzzification method. Defuzzification in remote sensing has to date, been performed by "crisp-assigning" each fuzzy-classified pixel or segment to the class for which it best fulfills the fuzzy classification rules, regardless of its classification fuzziness, uncertainty or ambiguity (maximum method). The defuzzification of an uncertain or ambiguous fuzzy classification leads to a more or less reliable crisp classification. In this paper the most common parameters for expressing classification uncertainty, fuzziness and ambiguity are analysed and discussed in terms of their ability to express the reliability of a crisp classification. This is done by means of a typical practical example from Object Based Image Analysis (OBIA).

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Section: Defuzzification Of Fuzzy Classification Resultsmentioning

confidence: 99%

Defuzzification Strategies for Fuzzy Classifications of Remote Sensing Data

Hofmann

2016

Remote Sensing

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“…is not verified, the Mean of Maximum or the Center of Gravity [17] defuzzification methods may provide a better performance. However, due to the widespread use of strongly partitioned fuzzy sets in the experiments with GPFISRegress, a normalized version of the Height Method (8) has been employed:…”

Section: If Only a Predictionμmentioning

confidence: 94%

A Multi-gene Genetic Programming Fuzzy Inference System for Regression Problems

Koshiyama¹,

Vellasco²,

Tanscheit³

2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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This work presents a novel Genetic Fuzzy System (GFS), called Genetic Programming Fuzzy Inference System for Regression problems (GPFISRegress). It makes use of Multi-Gene Genetic Programming to build the premises of fuzzy rules, including t-norms, negation and linguistic hedge operators. GPFIS-Regress also defines a consequent term that is more compatible with a given premise and makes use of aggregation operators to weigh fuzzy rules in accordance with their influence on the problem. The system has been applied to a set of benchmarks and has also been compared to other GFSs, showing competitive results in terms of accuracy and interpretability.

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“…The output fuzzy variable has to be defuzzified to a single crisp value so that it can be used in practice. In this study, the center of gravity method [21] was used for defuzzyfication. The value is given by Eq.…”

Section: Cluster Head Selection Phasementioning

confidence: 99%

An Energy Aware Unequal Clustering Algorithm using Fuzzy Logic for Wireless Sensor Networks

Adhikary

Mallick

2017

J. ICT Res. Appl.

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Abstract. In wireless sensor networks, clustering provides an effective way of organising the sensor nodes to achieve load balancing and increasing the lifetime of the network. Unequal clustering is an extension of common clustering that exhibits even better load balancing. Most existing approaches do not consider node density when clustering, which can pose significant problems. In this paper, a fuzzy-logic based cluster head selection approach is proposed, which considers the residual energy, centrality and density of the nodes. In addition, a fuzzy-logic based clustering range assignment approach is used, which considers the suitability and the position of the nodes in assigning the clustering range. Furthermore, a weight function is used to optimize the selection of the relay nodes. The proposed approach was compared with a number of well known approaches by simulation. The results showed that the proposed approach performs better than the other algorithms in terms of lifetime and other metrics.

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A survey of defuzzification strategies

Cited by 138 publications

References 14 publications

Defuzzification Strategies for Fuzzy Classifications of Remote Sensing Data

Defuzzification Strategies for Fuzzy Classifications of Remote Sensing Data

A Multi-gene Genetic Programming Fuzzy Inference System for Regression Problems

An Energy Aware Unequal Clustering Algorithm using Fuzzy Logic for Wireless Sensor Networks

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