FURIA: an algorithm for unordered fuzzy rule induction

Hühn, Jens Christian; Hüllermeier, Eyke

doi:10.1007/s10618-009-0131-8

Cited by 372 publications

(220 citation statements)

References 41 publications

(43 reference statements)

Supporting

Mentioning

217

Contrasting

Unclassified

Order By: Relevance

“…The unambiguous boundaries can often be fraudulent activities laying, but not exceeding, the threshold. The FURIA algorithm [11] presents an interesting proposal for dealing with this situation. This approach performs an adaptation of the RIPPER algorithm, allowing the computation of a first rule set.…”

Section: Related Workmentioning

confidence: 99%

A novel rule generator for intrusion detection based on frequent subgraph mining

Herrera-Semenets

Gago-Alonso

2017

Ingeniare. Rev. chil. ing.

View full text Add to dashboard Cite

The current development of technologies has boosted the use of telecommunication services. This fact brings an increase in the volumes of data generated in telecommunication companies. Such data need to be processed in order to detect potential intruders or fraud. The rule evaluation techniques are widely used in these application contexts due to their high effectiveness over known attacks. The incorporation of an automatic rule generator allows it to obtain rules in large volumes of data, for assisting information analysts; thus, the accuracy of intrusion detection is increased. In this paper, an automatic rule generation method is presented, including a strategy based on processing the patterns extracted from a training set and building classification rules. Finally, our proposal is evaluated and compared regarding other classifiers, achieving good results.Keywords: Automatic rule generation, frequent subgraph mining, intrusion detection. RESUMEN El desarrollo actual de las tecnologías ha impulsado el uso de servicios de telecomunicaciones. Este hecho implica un aumento en los volúmenes de datos generados en las empresas de telecomunicaciones. Dichos datos requieren ser procesados con el fin de detectar potenciales intrusos o fraudes. Las técnicas de evaluación regla son ampliamente utilizadas en estos contextos de aplicación por su alta efectividad sobre ataques conocidos. La incorporación de un generador automático de reglas en dichas técnicas

show abstract

Section: Related Workmentioning

confidence: 99%

A novel rule generator for intrusion detection based on frequent subgraph mining

Herrera-Semenets

Gago-Alonso

2017

Ingeniare. Rev. chil. ing.

View full text Add to dashboard Cite

show abstract

“…Fuzzy Unordered Rules Induction Algorithm (FU-RIA) [18,19] is an extension of the state-of-the-art rule learning algorithm called RIPPER [49], having its advantages such like simple and comprehensible Rule R j : If x 1 is A j1 and . .…”

Section: Furiamentioning

confidence: 99%

“…In [17] we extended our previous developments by proposing a fuzzy MCS framework based on Fuzzy Unordered Rule Induction Algorithm (FU-RIA) [18,19] as the fuzzy rule classification learning algorithm to derive the component classifiers considering bagging and feature selection. We conducted comprehensive experiments with 20 datasets taken from the UCI machine learning repository and provided a deep study of the results obtained.…”

Section: Introductionmentioning

confidence: 99%

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

Trawiński¹,

Cordón²,

Quirin³

2012

IJCIS

View full text Add to dashboard Cite

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.

show abstract

“…There exists a number of classification algorithms including Bayesian classifiers [12], nearest neighbor classifiers [11], rule-based classifiers [9], support vector machines [10], classification trees [6,26], neural classifiers [8,23], fuzzy logic-based classifiers [4,17,19] and many hybrid and ensemble methods [27,30].…”

Section: Introductionmentioning

confidence: 99%

Design of Fuzzy Rule-based Classifiers through Granulation and Consolidation

Riid

Preden

2017

Journal of Artificial Intelligence and Soft Computing Research

View full text Add to dashboard Cite

This paper addresses the issue how to strike a good balance between accuracy and compactness in classification systems -still an important question in machine learning and data mining. The fuzzy rule-based classification approach proposed in current paper exploits the method of rule granulation for error reduction and the method of rule consolidation for complexity reduction. The cooperative nature of those methods -the rules are split in a way that makes efficient rule consolidation feasible and rule consolidation itself is capable of further error reduction -is demonstrated in a number of experiments with nine benchmark classification problems. Further complexity reduction, if necessary, is provided by rule compression.

show abstract

FURIA: an algorithm for unordered fuzzy rule induction

Cited by 372 publications

References 41 publications

A novel rule generator for intrusion detection based on frequent subgraph mining

A novel rule generator for intrusion detection based on frequent subgraph mining

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

Design of Fuzzy Rule-based Classifiers through Granulation and Consolidation

Contact Info

Product

Resources

About