Weighting fuzzy classification rules using receiver operating characteristics (ROC) analysis

Zolghadri, Mansoor J.; Mansoori, Eghbal G.

doi:10.1016/j.ins.2006.12.009

Cited by 46 publications

(22 citation statements)

References 31 publications

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“…Fuzzy logic has been used for designing classification systems drastically [39,40]. The important advantage of fuzzy logic is its influential capability in managing uncertainty and vagueness [41].…”

Section: Literature Reviewmentioning

confidence: 99%

Credit Risk Prediction Using Fuzzy Immune Learning

Kamalloo

Abadeh

2014

Advances in Fuzzy Systems

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The use of credit has grown considerably in recent years. Banks and financial institutions confront credit risks to conduct their business. Good management of these risks is a key factor to increase profitability. Therefore, every bank needs to predict the credit risks of its customers. Credit risk prediction has been widely studied in the field of data mining as a classification problem. This paper proposes a new classifier using immune principles and fuzzy rules to predict quality factors of individuals in banks. The proposed model is combined with fuzzy pattern classification to extract accurate fuzzy if-then rules. In our proposed model, we have used immune memory to remember good B cells during the cloning process. We have designed two forms of memory: simple memory and k-layer memory. Two real world credit data sets in UCI machine learning repository are selected as experimental data to show the accuracy of the proposed classifier. We compare the performance of our immune-based learning system with results obtained by several well-known classifiers. Results indicate that the proposed immune-based classification system is accurate in detecting credit risks.

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Section: Literature Reviewmentioning

confidence: 99%

Credit Risk Prediction Using Fuzzy Immune Learning

Kamalloo

Abadeh

2014

Advances in Fuzzy Systems

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“…6 The SLAVE algorithm makes use of genetic algorithms to learn a fuzzy classifier Perez, 1999, 2001). 7 Both algorithms are frequently used for experimental purposes (e.g., (Fernández et al, 2007;Ishibuchi and Yamamoto, 2003;Cordon et al, 2004;Zolghadri and Mansoori, 2007)). …”

Section: Classification Accuracymentioning

confidence: 99%

FURIA: an algorithm for unordered fuzzy rule induction

Hühn

Hüllermeier

2009

Data Min Knowl Disc

372

214

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This paper introduces a novel fuzzy rule-based classification method called FURIA, which is short for Fuzzy Unordered Rule Induction Algorithm. FURIA extends the well-known RIPPER algorithm, a state-of-the-art rule learner, while preserving its advantages, such as simple and comprehensible rule sets. In addition, it includes a number of modifications and extensions. In particular, FURIA learns fuzzy rules instead of conventional rules and unordered rule sets instead of rule lists. Moreover, to deal with uncovered examples, it makes use of an efficient rule stretching method. Experimental results show that FURIA significantly outperforms the original RIPPER, as well as other classifiers such as C4.5, in terms of classification accuracy.

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“…Weights z q j of this type were studied in [48]. In [60], weights based on ROC analysis are advocated.…”

Section: The Fuzzy Modelmentioning

confidence: 99%

A general framework for designing a fuzzy rule-based classifier

et al. 2010

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This is an accepted version of a paper published in Knowledge and Information Systems. This paper has been peer-reviewed but does not include the final publisher proofcorrections or journal pagination.Citation for the published paper: verikas, a., guzaitis, j., gelzinis, a., bacauskiene, m. Abstract This paper presents a general framework for designing a fuzzy rule-based classifier. Structure and parameters of the classifier are evolved through a two-stage genetic search. To reduce the search space, the classifier structure is constrained by a tree created using the evolving SOM tree algorithm. Salient input variables are specific for each fuzzy rule and are found during the genetic search process. It is shown through computer simulations of four real world problems that a large number of rules and input variables can be eliminated from the model without deteriorating the classification accuracy. By contrast, the classification accuracy of unseen data is increased due to the elimination.

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Weighting fuzzy classification rules using receiver operating characteristics (ROC) analysis

Cited by 46 publications

References 31 publications

Credit Risk Prediction Using Fuzzy Immune Learning

Credit Risk Prediction Using Fuzzy Immune Learning

FURIA: an algorithm for unordered fuzzy rule induction

A general framework for designing a fuzzy rule-based classifier

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