A general framework for designing a fuzzy rule-based classifier

Verikas, Antanas; Guzaitis, Jonas; Gelžinis, Adas; Bačauskienė, Marija

doi:10.1007/s10115-010-0340-x

Cited by 25 publications

(13 citation statements)

References 67 publications

(79 reference statements)

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“…In Table 7, Roubos' algorithm [1], TSFR [2], SC-BPN [3], DDFC [4], GA-BPN [5], FRS-OC-GA [6], HFRBCS [7], Zhou [8]'s algorithm, and FOF [14], populate the following columns. Compared with other classification experiments, our method gives the best classification rate for three of the five databases.…”

Section: Resultsmentioning

confidence: 99%

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Advanced Hierarchical Fuzzy Classification Model Adopting Symbiosis Based DNA-ABC Optimization Algorithm

Feng¹,

Li²

2016

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This paper offers a symbiosis based hybrid modified DNA-ABC optimization algorithm which combines modified DNA concepts and artificial bee colony (ABC) algorithm to aid hierarchical fuzzy classification. According to literature, the ABC algorithm is traditionally applied to constrained and unconstrained problems, but is combined with modified DNA concepts and implemented for fuzzy classification in this present research. Moreover, from the best of our knowledge, previous research on the ABC algorithm has not combined it with DNA computing for hierarchical fuzzy classification to explore the merits of cooperative coevolution. Therefore, this paper is the first to apply the mechanism of symbiosis to create a hybrid modified DNA-ABC algorithm for hierarchical fuzzy classification applications. In this study, the partition number and the shape of the membership function are extracted by the symbiosis based hybrid modified DNA-ABC optimization algorithm, which provides both sufficient global exploration and also adequate local exploitation for hierarchical fuzzy classification. The proposed optimization algorithm is applied on five benchmark University of Irvine (UCI) data sets, and the results prove the efficiency of the algorithm.

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Section: Resultsmentioning

confidence: 99%

“…fuzzy or neural networks, are proposed to figure out these issues. For instance, labeled data can be used to establish fuzzy rule-based classification system by GA [1] or SOM tree algorithm [2]. SC-BPN [3] attempts to construct a classifier based on statistical normalization and back propagation.…”

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confidence: 99%

Advanced Hierarchical Fuzzy Classification Model Adopting Symbiosis Based DNA-ABC Optimization Algorithm

Feng¹,

Li²

2016

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“…In contrast to crisp classification, which leads to crisp decisions about the data, the fuzzy classification allows a more sensitive data analysis [2]. Fuzzy decision trees and if-then rules are an example of efficient, transparent, and easily interpretable fuzzy classifiers [2,25].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary prediction of photovoltaic power plant energy production

Krömer

Prokop

Snášel

et al. 2012

Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation

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This paper presents an application of genetic programming to the evolution of fuzzy predictors based on fuzzy information retrieval. The fuzzy predictors are used to estimate the output of a Photovoltaic Power Plant (PVPP). The PVPPs are energy sources with an unstable production of electrical energy. It is necessary to back up the energy produced by the PVPPs for stable electric network operations. An optimal value of backup power can be set with advanced prediction models that can contribute to the robustness of the electric network within the framework of an intelligent power grid. This work extends previous research on evolutionary design of fuzzy PVPP output predictors by the evaluation of the method on a larger data set describing the operations of a real PVPP.

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“…The Classification Techniques (Verikas, Guzaitis, Gelzinis, & Bacauskiene, 2011) is part of the Data Mining Technology that classifies data in different classes. The fuzzy logic used to classify uncertain or ambiguous data.…”

Section: Related Workmentioning

confidence: 99%

A Novel Data Classification Approach Using Fuzzy Logic

AL-Jallad¹,

Xiaoli²,

Khairalla³

et al. 2017

International Symposium on Computer Science and Artificial Intelligence(ISCSAI)

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Data Mining is the core part of knowledge discovery aims to extract hidden patterns from data. It is mainly used in information processing applications. It is known that the classification techniques divide data based on predefined class-labels. The proposed algorithm classifies the student lending institutions decisions of requested borrowings and financial assistance into three clusters: accepted, rejected, and probably accepted. Our new classification algorithm based on fuzzy rules, where a fuzzy logic based approach provides suitable trend to handle classification problems. Our algorithm predicts the acceptance of the students' applications on the basis of ranking and using fuzzy rules which generates a numerical data. The output of the proposed algorithm in form of IF-THEN. Set of the state of art algorithms such as KNN, Fuzzy C-Means used for comparison. The experimental results showed that the proposed algorithm achieved "better" values in term of model accuracy.

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A general framework for designing a fuzzy rule-based classifier

Cited by 25 publications

References 67 publications

Advanced Hierarchical Fuzzy Classification Model Adopting Symbiosis Based DNA-ABC Optimization Algorithm

Advanced Hierarchical Fuzzy Classification Model Adopting Symbiosis Based DNA-ABC Optimization Algorithm

Evolutionary prediction of photovoltaic power plant energy production

A Novel Data Classification Approach Using Fuzzy Logic

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