A proposal on reasoning methods in fuzzy rule-based classification systems

Cordón, Óscar; Jesus, María José del; Herrera, Francisco

doi:10.1016/s0888-613x(00)88942-2

Cited by 353 publications

(169 citation statements)

References 20 publications

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“…Another way is to use various methods to achieve an automatic adaptation of Fuzzification. These methods are generally based on genetic algorithms 6,27 or clustering algorithms 8 . However, these techniques require a large amount of data during the learning process.…”

Section: Fuzzy Linguistic Variable Definitionmentioning

confidence: 99%

Using fuzzy rules for network behavior identification: application for differentiated services in an Ethernet network

Bombardier¹,

Georges²,

Rondeau³

et al. 2018

IJCIS

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The Quality of Service (QoS) offered by a network is based on criteria such as delay, jitter and message loss. QoS management is crucial to ensure that the network respects the communication constraints defined by the application requirements. The network must dynamically manage QoS according to the evolution of network usage. To control QoS, the network implements mechanisms to schedule and regulate traffic. The main issue is identifying the relationship between these mechanisms and their effects on QoS values. The objective of this paper is to propose a method, based on fuzzy logic, for identifying this relationship and reusing it to dynamically control QoS. This study only focuses on the impact of the Weighted Round Robin (WRR) scheduler on the delay in a switched Ethernet network. The fuzzy controller is implemented in a network simulation tool (Riverbed Modeler) to efficiently manage the delay in the network.

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Section: Fuzzy Linguistic Variable Definitionmentioning

confidence: 99%

Using fuzzy rules for network behavior identification: application for differentiated services in an Ethernet network

Bombardier¹,

Georges²,

Rondeau³

et al. 2018

IJCIS

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“…The vote strength of the rule is calculated as the product of the firing degree µ C j r (x) and the certainty degree CD j . Hence, the fuzzy reasoning method used is the so-called voting-based method [50,51].…”

Section: Furiamentioning

confidence: 99%

A Study on the Use of Multiobjective Genetic Algorithms for Classifier Selection in FURIA-based Fuzzy Multiclassifiers

Trawiński¹,

Cordón²,

Quirin³

2012

IJCIS

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In a preceding contribution, we conducted a study considering a fuzzy multiclassifier system (MCS) design framework based on Fuzzy Unordered Rule Induction Algorithm (FURIA). It served as the fuzzy rule classification learning algorithm to derive the component classifiers considering bagging and feature selection. In this work, we integrate this approach under the overproduce-and-choose strategy. A state-ofthe-art evolutionary multiobjective algorithm, namely NSGA-II, is used to provide a component classifier selection and improve FURIA-based fuzzy MCS. We propose five different fitness functions based on three different optimization criteria, accuracy, complexity, and diversity. Twenty UCI high dimensional datasets were considered in order to conduct the experiments. A combination between accuracy and diversity criteria provided very promising results, becoming competitive with classical MCS learning methods.

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“…The subscripts in %Test are related to the fuzzy reasoning method (FRM) [3] used, so 1 corresponds to the classical FRM (max-min) and 2 with the normalised sum respectively (except in the C4.5 algorithm in where FRM is not used, so it has been decided to place the results in the first of the last two columns).…”

Section: Experimental Studymentioning

confidence: 99%

A Genetic-Programming-Based Approach for the Learning of Compact Fuzzy Rule-Based Classification Systems

Berlanga

Jesús

Gacto

et al. 2006

Artificial Intelligence and Soft Computing – ICAISC 2006

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Abstract. In the design of an interpretable fuzzy rule-based classification system (FRBCS) the precision as much as the simplicity of the extracted knowledge must be considered as objectives. In any inductive learning algorithm, when we deal with problems with a large number of features, the exponential growth of the fuzzy rule search space makes the learning process more difficult. Moreover it leads to an FRBCS with a rule base with a high cardinality. In this paper, we propose a genetic-programming-based method for the learning of an FRBCS, where disjunctive normal form (DNF) rules compete and cooperate among themselves in order to obtain an understandable and compact set of fuzzy rules, which presents a good classification performance with high dimensionality problems. This proposal uses a token competition mechanism to maintain the diversity of the population. The good results obtained with several classification problems support our proposal.

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A proposal on reasoning methods in fuzzy rule-based classification systems

Cited by 353 publications

References 20 publications

Using fuzzy rules for network behavior identification: application for differentiated services in an Ethernet network

Using fuzzy rules for network behavior identification: application for differentiated services in an Ethernet network

A Study on the Use of Multiobjective Genetic Algorithms for Classifier Selection in FURIA-based Fuzzy Multiclassifiers

A Genetic-Programming-Based Approach for the Learning of Compact Fuzzy Rule-Based Classification Systems

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