A Fuzzy Inference System for Credit Scoring using Boolean Consistent Fuzzy Logic

Latinović, Milica; Dragović, Ivana; Arsić, Vesna Bogojević; Petrović, Bratislav

doi:10.2991/ijcis.11.1.31

Cited by 8 publications

(6 citation statements)

References 38 publications

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“…IBA has a wide range of applications from data clustering [45,46] to ranking and decision-making [47,48]. The most prominent application of IBA involves data aggregation for tasks of ranking, selection, or prediction.…”

Section: Interpolative Boolean Algebramentioning

confidence: 99%

IBA-VNS: A Logic-Based Machine Learning Algorithm and Its Application in Surgery

Čolić,

Milošević,

Dragović

et al. 2024

Mathematics

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The interpretability and explainability of machine learning (ML) approaches play a key role in the trustworthiness of ML models in various applications. The objective of this paper is to incorporate a logic-based reasoning in the ML model that is not only accurate but also interpretable and easily applied. More precisely, we propose a hybrid IBA-VNS approach based on interpolative Boolean algebra (IBA) and variable neighborhood search (VNS). IBA is chosen over traditional multi-valued and/or fuzzy logic techniques due to its consistency in preserving all Boolean axioms. The VNS heuristic is used for model training, i.e., determining the optimal logical aggregation function within the IBA framework for solving observed prediction problems. Obtained logic aggregation functions are easy to understand and may provide additional insight to the decision-maker. The proposed approach does not require any domain knowledge and is applicable in various domains. IBA-VNS is evaluated on several standard datasets. Further, IBA-VNS is applied to the real-world problem of predicting hospital length of stay (LOS), showing exceptional results in terms of interpretability and accuracy. In fact, the dataset is collected from the LabSerb program regarding colorectal surgeries in the period 2015–2023. The proposed approach extracted knowledge regarding the problem, i.e., the causal relations between the patient’s health condition and LOS, along with achieving an MAE of 1.144 days.

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Section: Interpolative Boolean Algebramentioning

confidence: 99%

IBA-VNS: A Logic-Based Machine Learning Algorithm and Its Application in Surgery

Čolić,

Milošević,

Dragović

et al. 2024

Mathematics

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“…The essence of deep learning developed from neural networks, and since deep learning has more layers of hidden layers than traditional neural networks, the more layers the more the essence of the object can be abstracted, which helps to see the distribution of data features [11]. The scientific processing of data is an important part of the use of modern nuclear analysis techniques to study ancient ceramics; multivariate statistical analysis methods use mathematical and statistical principles to analyse multivariate problems; especially with the upgrading and development of computer technology and innovation, multivariate statistical analysis techniques have been widely used in the processing of data in more high-end subject areas; some of the analysis techniques in multivariate statistical methods have become highly practical, simple, and effective modern processing methods [12]. In this paper, multivariate statistical analysis methods are used to deal with multivariate data analysis problems, and common multivariate statistical analysis methods include ANOVA, scatter analysis, factor analysis, discriminant analysis, and fuzzy cluster analysis, to reasonably classify the tires and glazes of ceramic samples of different origins and help categorize the genus of ceramic samples; for example, fuzzy cluster analysis uses a quantitative mathematical modelling method, according to a batch of analysis objects of multiple observations to find out some specific statistic confidence level that can measure the sparsity of each other, and use the characteristics of fuzzy mathematical linear algebra transpose symmetry to establish fuzzy line comparison relationship and classify the research objects [13].…”

Section: Status Of Researchmentioning

confidence: 99%

Using Image Feature Extraction to Identification of Ancient Ceramics Based on Partial Differential Equation

Niu

Zhang

2022

Advances in Mathematical Physics

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This paper presents an in-depth study and analysis of the image feature extraction technique for ancient ceramic identification using an algorithm of partial differential equations. Image features of ancient ceramics are closely related to specific raw material selection and process technology, and complete acquisition of image features of ancient ceramics is a prerequisite for achieving image feature identification of ancient ceramics, since the quality of extracted area-grown ancient ceramic image feature extraction method is closely related to the background pixels and does not have generalizability. In this paper, we propose a deep learning-based extraction method, using Eased as a deep learning support platform, to extract and validate 5834 images of 272 types of ancient ceramics from kilns, celadon, and Yue kilns after manual labelling and training learning, and the results show that the average complete extraction rate is higher than 99%. The implementation of the deep learning method is summarized and compared with the traditional region growth extraction method, and the results show that the method is robust with the increase of the learning amount and has generalizability, which is a new method to effectively achieve the complete image feature extraction of ancient ceramics. The main content of the finite difference method is to use the ratio of the difference between the function values of two adjacent points and the distance between the two points to approximate the partial derivative of the function with respect to the variable. This idea was used to turn the problem of division into a problem of difference. Recognition of ancient ceramic image features was realized based on the extraction of the overall image features of ancient ceramics, the extraction and recognition of vessel type features, the quantitative recognition of multidimensional feature fusion ornamentation image features, and the implementation of deep learning based on inscription model recognition image feature classification recognition method; three-layer B/S architecture web application system and cross-platform system language called as the architectural support; and database services, deep learning packaging, and digital image processing. The specific implementation method is based on database service, deep learning encapsulation, digital image processing, and third-party invocation, and the service layer fusion and relearning mechanism is proposed to achieve the preliminary intelligent recognition system of ancient ceramic vessel type and ornament image features. The results of the validation test meet the expectation and verify the effectiveness of the ancient ceramic vessel type and ornament image feature recognition system.

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“…A Boolean matrix contains information that are derived from the outputs and inputs assigned through rules that are established from if-then rules. Binary values of 1 are applied in the Boolean matrix to show the presence of rules between inputs and outputs and 0 indicates no rules presence [16].…”

Section: Fuzzy Networkmentioning

confidence: 99%

“…2 in order to form the antecedent and consequent matrices of both BS and CS as performed in Eq. (16)(17)(18)(19)(20)(21)(22)(23).…”

Section: Case Study: Stock Evaluationmentioning

confidence: 99%

Z-Hesitant Fuzzy Network Model with Reliability and Transparency of Information for Decision Systems

Yaakob

Shafie

Gegov

et al. 2021

Int J Comput Intell Syst

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Decision-making environment often encounters complexity along its processes, especially in the context of multidisciplinary scientific research. This can commonly be seen in engineering, computing, finance, astrology and other different areas. It is of great restriction in dealing with the practical problems which have diverse demands and properties. There is a growing body of literature that recognizes the importance of dealing with the complexity in decision making environment. The reliability and the transparency are the dominant feature of the integration of fuzzy network and Z-numbers. However, much of the research up to now has been descriptive in nature of the features. Hence, this proposed method is unique and novel because it offers some interesting insight of dealing with reliability and transparency of information in Z-hesitant fuzzy network decision-making environment. The fuzzy networks have the functionality under rule bases of fuzzy systems where it is recognized by its transparency and precision. The proposed method makes use of fuzzy network with the incorporation of hesitant fuzzy sets to assimilate decision information towards alternatives. For the validation and applicability purposes of the proposed method, the case study of stock evaluation assessed by a number of decision makers has been utilized as a real-world problem. The performance of the proposed method is evaluated respectively by applying the Spearman’s rho correlation. The result shows that the proposed method performs as the established method with the consideration of additional dominant features.

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A Fuzzy Inference System for Credit Scoring using Boolean Consistent Fuzzy Logic

Cited by 8 publications

References 38 publications

IBA-VNS: A Logic-Based Machine Learning Algorithm and Its Application in Surgery

IBA-VNS: A Logic-Based Machine Learning Algorithm and Its Application in Surgery

Using Image Feature Extraction to Identification of Ancient Ceramics Based on Partial Differential Equation

Z-Hesitant Fuzzy Network Model with Reliability and Transparency of Information for Decision Systems

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