An Optimal Prediction Model’s Credit Risk: The Implementation of the Backward Elimination and Forward Regression Method

Haloui, Sara; Moudden, Abdeslam El

doi:10.14569/ijacsa.2020.0110259

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…A quadratic regression model was initially suggested and adopted as its R-squared statistic is above 90%. In the analysis of variance (ANOVA), Backward Elimination method was used to improve and determine the final modified regression model [14].…”

Section: Resultsmentioning

confidence: 99%

Optimization of Commercial Silica Gel Regeneration via Response Surface Methodology

Ong,

Chua

2024

AEJ

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Silica gel, a highly porous form of silicon dioxide, is widely used as a desiccant due to its hygroscopic properties and superior characteristics compared to organic materials. Although silica gel can be regenerated and reused, conventional methods often involve prolonged heating and limited ventilation, resulting in energy waste and reduced efficiency. To optimize the regeneration process, this paper presents an experimental approach to regenerate silica gel using response surface methodology (RSM). The study explores the influence of temperature, duration, and natural convection rate, which is regulated by adjusting the vent size of the oven. The results indicate that saturated silica gel at 98-100% equilibrium relative humidity (RH) can be effectively regenerated to nearly its dry weight by applying a temperature of 70°C for a duration of 10 minutes in a cubic oven measuring 15cm on each side, with an approximate vent area of 225cm2. Moreover, assigning different levels of importance to the influencing factors in the Design Expert software allows calibration of these factors to achieve the desired equilibrium RH.

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Section: Resultsmentioning

confidence: 99%

Optimization of Commercial Silica Gel Regeneration via Response Surface Methodology

Ong,

Chua

2024

AEJ

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“…This process continues until a model is found that only contains variables that significantly affect the dependent variable. [21], [22] 2) Forward Selection Forward Selection is the opposite of the Backward Elimination method, where features or independent variables are selected in stages. The independent variables are entered into the model in stages, starting with the variable with the highest correlation to the positive and negative dependent variables.…”

Section: ) Backward Eliminationmentioning

confidence: 99%

House Price Prediction using Multiple Linear Regression and KNN

Febriyanto,

Endroyono,

Kusnendar

2023

JAREE

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The transition of BPHTB management from central taxes to regional taxes is a continuation of the regional autonomy policy. The difference between the market value and the prevailing NJOP poses a challenge for the Sintang District Government in determining the Tax Object Acquisition Value (NPOP) as the basis for imposing BPHTB. Machine learning has been extensively explored for predictions and can be an alternative that can help predict NPOP, especially house prices. This study uses backward elimination and forward selection methods to select the features used in this study and multiple linear regression and K-Nearest Neighbor methods to make house price prediction models. The results of model performance measurement using RMSE, Multiple Linear Regression method with feature selection using backward elimination resulted in a better model with an RMSE value of 44.02 (million rupiahs) and an R2 value of 0.707.

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“…CRA systems appeared at the beginning of the 2000s, with the development of new technologies and financial institutions' need for information systems that could conduct CRA. The first CRA systems implemented algorithms and statistical techniques based on financial models to determine potential debtors [17]. Later, ML-based models appeared and evolved into AI models that use big data techniques to process big databases, which is necessary to improve the results of the assessment [18].…”

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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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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An Optimal Prediction Model’s Credit Risk: The Implementation of the Backward Elimination and Forward Regression Method

Cited by 4 publications

References 4 publications

Optimization of Commercial Silica Gel Regeneration via Response Surface Methodology

Optimization of Commercial Silica Gel Regeneration via Response Surface Methodology

House Price Prediction using Multiple Linear Regression and KNN

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

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