New hybrid method for feature selection and classification using meta-heuristic algorithm in credit risk assessment

Khiarak, Jalil Nourmohammadi; Feizi‐Derakhshi, Mohammad‐Reza; Razeghi, Fatemeh; Mazaheri, Samaneh; Zamani-Harghalani, Yashar; Moosavi-Tayebi, Rohollah

doi:10.1007/s42044-019-00038-x

Cited by 10 publications

(4 citation statements)

References 21 publications

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“…In turn, Figure 4 shows the countries where the articles included in this SLR were published. Again, most of them were published in China (33), followed by India (5). Table 4 details all the keywords retrieved from the selected documents.…”

Section: Total 660mentioning

confidence: 99%

“…Different systems of this kind have been increasingly used, applying many current technologies. Several banks are developing their own systems based on their own selection criteria to reduce the probability of losses in loans requested by customers or firms [5]. Some of the most widely used CRA systems are based on Artificial Intelligence (AI) and Machine Learning (ML) techniques that analyze and pinpoint trends in potential debtors [6].…”

Section: Introductionmentioning

confidence: 99%

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Modelos para la evaluación de riego crediticio en el ámbito de la tecnología financiera: una revisión

Tadeo Espinoza,

Coral Ygnacio

2023

TecnoL.

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Esta revisión analiza una selección de artículos científicos sobre la implantación de sistemas de evaluación del riesgo de crédito para identificar las soluciones existentes, las más acertadas y las limitaciones y problemas en su desarrollo. Se adoptó la declaración PRISMA del siguiente modo: se formularon las preguntas de investigación, se definieron los criterios de inclusión, se seleccionaron las palabras clave y se diseñó la cadena de búsqueda. Por último, se calcularon varios estadísticos descriptivos de los artículos seleccionados. En los estudios seleccionados se identificaron 31 soluciones, entre métodos, modelos y algoritmos. Algunos de los modelos más utilizados se basan en técnicas de Inteligencia Artificial (IA), especialmente Redes Neuronales y Bosques Aleatorios. Se concluyó que las Redes Neuronales son las soluciones más eficientes, con precisiones medias superiores al 90 %, pero su desarrollo puede tener limitaciones. Estas soluciones deben implementarse teniendo en cuenta el contexto en el que se van a emplear.

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Section: Total 660mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modelos para la evaluación de riego crediticio en el ámbito de la tecnología financiera: una revisión

Tadeo Espinoza,

Coral Ygnacio

2023

TecnoL.

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show abstract

“…The results demonstrate how the mutation operator increases the exibility and effectiveness of the algorithm. Also, Nourmohammadi-Khiarak et al [40] produced a competitive algorithm to de ne an optimum subset of features, imperialist competitive algorithm with modi ed fuzzy min -max classi er (ICA-MFMCN) is given. Performance of the proposed classi cation ICA-MFMCN is approved and recognized a real credit set selected from a UCI dataset.…”

Section: Related Workmentioning

confidence: 99%

Heap Based Optimizer Algorithm for Solving Feature Selection Problems in High-Dimensional Cancer Microarray Data

Alweshah

Jebril

Alkhalaileh

et al. 2022

Preprint

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Feature selection (FS) is an important preprocessing step that has been commonly used in several fields to improve the performance of learning algorithms. In the field of medical data mining, a huge number of features are used in diagnosing disease, but these features have a lot of non-relevant weak correlations and redundant characteristics, which causes a number of problems that adversely affect diagnostic predictive accuracy. Work on FS has grown extensively many fields due to increased demand for methods that can reduce the dimensionality of data by choosing the best subset of features according to specific criteria in order to maximize prediction accuracy and minimize irrelevant features. In recent times, metaheuristics have been preferred over conventional optimization methods for solving FS problems in order to try to obtain an almost optimal solution in a finite time. Metaheuristics are general-purpose algorithms that can be used to solve almost any optimization problem because they generate “appropriate” solutions in a reasonable amount of time, which is especially useful when seeking to solve complicated problems. Many popular implementations have shown the utility of metaheuristics in different ways by contrasting their performance on well-known problems with that of other algorithms or applications .There are many metaheuristic algorithms in the literature such as those based on swarm intelligence, including particle swarm optimizations and ant colony optimization. The major objective of this research is to provide an increased degree of accuracy to resolve FS problems by conducting different experiments using a metaheuristic algorithm, namely the heap-based optimizer algorithm (HBO). The HBO is used with a k-nearest neighbor classifier in a wrapper to improve the FS process. The performance of the proposed method is evaluated and compared against seven approaches in the literature that are applied on nine high-dimensional data sets that contain, a low number of samples and multiple classes. The findings reveal that the HBO decreases the number of features for classification tasks, and is able to achieve high accuracy in two data sets as compared to the other approaches, the BHBO achieved the best convergence speed as compared to the competing methods. It is therefore concluded that the proposed HBO method can be used to optimize the FS process, whether in terms of classification accuracy or selection size.

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“…The utilization of metaheuristic algorithms such as the BBA for feature selection optimization problems is not new. Many studies reported the success of metaheuristic algorithms in solving combinatorial problems, as demonstrated in [10], [11]. There are many types of metaheuristic algorithms available for consideration that are categorized into four according to their search behaviors, which are evolution-based, swarm intelligence-based, physics-based, and human-related algorithms [12].…”

Section: Introductionmentioning

confidence: 99%

Taguchi's T-method with nearest integer-based binary bat algorithm for prediction

Marlan

Jamaludin²,

Ramlie³

et al. 2022

Bulletin EEI

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Taguchi’s T-method is a new prediction technique under the Mahalanobis-Taguchi system to predict unknown output or future states based on available historical information. Conventionally, in optimizing the T-method prediction accuracy, Taguchi’s orthogonal array is utilized to determine a subset of significant features to be used in formulating the optimal prediction model. This, however, resulted in a sub-optimal prediction accuracy due to its fixed and limited feature combination offered for evaluation and lack of higher-order feature interaction. In this paper, a swarm-based binary bat optimization algorithm with a nearest integer discretization approach is integrated with the Taguchi’s T-method. A comparative study is conducted by comparing the performance of the proposed method against the conventional approach using mean absolute error as the performance measure on four benchmark case studies. The results from experimental studies show a significant improvement in the T-method prediction accuracy. A reduction in the total number of features results in a less complex model. Based on the general observation, the nearest integer-based binary bat algorithm successfully optimized the selection of significant features due to recursive and repetitive searchability, in addition to its adaptive element in response to the current best solution in guiding the search process towards optimality.

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New hybrid method for feature selection and classification using meta-heuristic algorithm in credit risk assessment

Cited by 10 publications

References 21 publications

Modelos para la evaluación de riego crediticio en el ámbito de la tecnología financiera: una revisión

Modelos para la evaluación de riego crediticio en el ámbito de la tecnología financiera: una revisión

Heap Based Optimizer Algorithm for Solving Feature Selection Problems in High-Dimensional Cancer Microarray Data

Taguchi's T-method with nearest integer-based binary bat algorithm for prediction

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