Rule-based credit risk assessment model using multi-objective evolutionary algorithms

Soui, Makram; Gasmi, Ines; Smiti, Salima; Ghédira, Khaled

doi:10.1016/j.eswa.2019.01.078

Cited by 59 publications

(29 citation statements)

References 44 publications

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“…In order to make the comparative values, reduce the searching space in the learning processes and make sure that there is no big imbalance between ( ) and ( ), and between (L j ) and (V j ), the constraints on the semantic parameter values should be the same as the ones used in the compared methods (in [13]) and they are applied as follows: the number of both negative and positive hedges is 1, the negative hedge is "Less" (L) and the positive hedge is "Very"…”

Section: Resultsmentioning

confidence: 99%

“…The fuzzy rule based classifiers (FRBCs) have been studied intensively in the data mining field and has achieved a lot of successful results [1][2][3][4][5][6][7][8][9][10][11][12][13]. The advantage of this classification model is that the end-users can use the high interpretability fuzzy rule based knowledge extracted automatically from data in the form of if-then sentences as their knowledge.…”

Section: Introductionmentioning

confidence: 99%

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A Hedge Algebras Based Reasoning Method for Fuzzy Rule Based Classifier

Phong

Dư

Thông

2019

JST

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The fuzzy rule based classifier (FRBC) design methods have intensively been being studied during last years. The ones designed by utilizing hedge algebras as a formalism to generate the optimal linguistic values along with their (triangular and trapezoidal) fuzzy sets based semantics for the FRBCs have been proposed. Those design methods generate the fuzzy sets based semantics because the classification reasoning method still bases on the fuzzy set theory. One question which has been arisen is whether there is a pure hedge algebras classification reasoning method so that the fuzzy sets based semantic of the linguistic values in the fuzzy rule bases can be replaced with the hedge algebras based semantic. This paper answers that question by presenting a fuzzy rule based classifier design method based on hedge algebras with a pure hedge algebras classification reasoning method. The experimental results over 17 real world datasets are compared to the existing methods based on hedge algebras and fuzzy sets theory showing that the proposed method is effective and produces good results.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hedge Algebras Based Reasoning Method for Fuzzy Rule Based Classifier

Phong

Dư

Thông

2019

JST

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“…Soui, Gasmi, Smiti and Ghédira [13] mentioned about Multi-objective Evolutionary algorithm which was used to analyze rule-based credit risk models (SMOPSO, NSAG-II, MOEA / D and SPEA-2) which was evaluated based on 5 performance criteria -Comprehensibility, Fidelity, Accuracy, Scalability, and Generality which were applied on German and Australian data set. This research work analyses the various machine learning classifiers for successful credit risk evaluation.…”

Section: Credit Risk Assessment Using Machine Learning Techniquesmentioning

confidence: 99%

Credit Risk Assessment using Machine Learning Techniques

Aithal¹,

Jathanna²

2019

IJITEE

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Analysis of credit scoring is an effective credit risk assessment technique, which is one of the major research fields in the banking sector. Machine learning has a variety of applications in the banking sector and it has been widely used for data analysis. Modern techniques such as machine learning have provided a self-regulating process to analyze the data using classification techniques. The classification method is a supervised learning process in which the computer learns from the input data provided and makes use of this information to classify the new dataset. This research paper presents a comparison of various machine learning techniques used to evaluate the credit risk. A credit transaction that needs to be accepted or rejected is trained and implemented on the dataset using different machine learning algorithms. The techniques are implemented on the German credit dataset taken from UCI repository which has 1000 instances and 21 attributes, depending on which the transactions are either accepted or rejected. This paper compares algorithms such as Support Vector Network, Neural Network, Logistic Regression, Naive Bayes, Random Forest, and Classification and Regression Trees (CART) algorithm and the results obtained show that Random Forest algorithm was able to predict credit risk with higher accuracy

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“…In the FRBC design based on the fuzzy set theory approaches [1,2,6,7,21,22,23,24,35,36,38,39,41], the fuzzy partitions from which fuzzy rules are extracted are commonly pre-designed using fuzzy sets and then linguistic terms are intuitively assigned to fuzzy sets. Furthermore, fuzzy partitions can be generated automatically from data by using discretization or granular computing mechanisms [37].…”

Section: Introductionmentioning

confidence: 99%

“…No matter how they are designed, the problem of the linguistic term design is not clearly studied although fuzzy rule bases are represented by linguistic terms with their fuzzy set based semantics. Many techniques have been proposed to achieve compact fuzzy rule systems with accuracy and interpretability trade-off extracted from data, such as using artificial neural network [33] or genetic algorithm [1,2,7,21,36,38,39,41] by adjusting fuzzy set parameters to achieve the optimal fuzzy partitions and to select the optimal fuzzy rule based systems. However, the fuzzy set based semantics of linguistic terms are not preserved, leading to the affectedness of the interpretability of the fuzzy rule bases of classifiers.…”

Section: Introductionmentioning

confidence: 99%

A Hedge Algebras Based Classification Reasoning Method With Multi-Granularity Fuzzy Partitioning

Phong

Dư

Thuy

et al. 2019

JCC

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During last years, lots of the fuzzy rule based classifier (FRBC) design methods have been proposed to improve the classification accuracy and the interpretability of the proposed classification models. Most of them are based on the fuzzy set theory approach in such a way that the fuzzy classification rules are generated from the grid partitions combined with the pre-designed fuzzy partitions using fuzzy sets. Some mechanisms are studied to automatically generate fuzzy partitions from data such as discretization, granular computing, etc. Even those, linguistic terms are intuitively assigned to fuzzy sets because there is no formalisms to link inherent semantics of linguistic terms to fuzzy sets. In view of that trend, genetic design methods of linguistic terms along with their (triangular and trapezoidal) fuzzy sets based semantics for FRBCs, using hedge algebras as the mathematical formalism, have been proposed. Those hedge algebras-based design methods utilize semantically quantifying mapping values of linguistic terms to generate their fuzzy sets based semantics so as to make use of fuzzy sets based-classification reasoning methods proposed in design methods based on fuzzy set theoretic approach for data classification. If there exists a classification reasoning method which bases merely on semantic parameters of hedge algebras, fuzzy sets-based semantics of the linguistic terms in fuzzy classification rule bases can be replaced by semantics - based hedge algebras. This paper presents a FRBC design method based on hedge algebras approach by introducing a hedge algebra- based classification reasoning method with multi-granularity fuzzy partitioning for data classification so that the semantic of linguistic terms in rule bases can be hedge algebras-based semantics. Experimental results over 17 real world datasets are compared to existing methods based on hedge algebras and the state-of-the-art fuzzy sets theoretic-based approaches, showing that the proposed FRBC in this paper is an effective classifier and produces good results.

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Rule-based credit risk assessment model using multi-objective evolutionary algorithms

Cited by 59 publications

References 44 publications

A Hedge Algebras Based Reasoning Method for Fuzzy Rule Based Classifier

A Hedge Algebras Based Reasoning Method for Fuzzy Rule Based Classifier

Credit Risk Assessment using Machine Learning Techniques

A Hedge Algebras Based Classification Reasoning Method With Multi-Granularity Fuzzy Partitioning

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