Classifying a Lending Portfolio of Loans with Dynamic Updates via a Machine Learning Technique

Soleymani, Fazlollah; Masnavi, Houman; Shateyi, Stanford

doi:10.3390/math9010017

Cited by 9 publications

(4 citation statements)

References 33 publications

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“…Similar results have been obtained. Similar results were concluded in different settings [13][14][15][16][17][18][19][20][21][22][23] when handling missing data using ML/AI techniques, depends on the scenario and type of the data structure to be imputed.…”

Section: Discussionsupporting

confidence: 72%

“…In the last decade, the use of artificial intelligence (AI) and machine-learning (ML) techniques has increased substantially in a diverse range of disciplines, such as climatology, industry, and biomedicine [13][14][15][16][17][18][19][20]. There is a vast and diverse number of ML algorithms which may be mostly categorized based on the approach taken toward the data set as well as the type of data processed.…”

Section: Introductionmentioning

confidence: 99%

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Imputation for Repeated Bounded Outcome Data: Statistical and Machine-Learning Approaches

Larracoechea

Borges

2021

Mathematics

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Real-life data are bounded and heavy-tailed variables. Zero-one-inflated beta (ZOIB) regression is used for modelling them. There are no appropriate methods to address the problem of missing data in repeated bounded outcomes. We developed an imputation method using ZOIB (i-ZOIB) and compared its performance with those of the naïve and machine-learning methods, using different distribution shapes and settings designed in the simulation study. The performance was measured employing the absolute error (MAE), root-mean-square-error (RMSE) and the unscaled mean bounded relative absolute error (UMBRAE) methods. The results varied depending on the missingness rate and mechanism. The i-ZOIB and the machine-learning ANN, SVR and RF methods showed the best performance.

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Section: Discussionsupporting

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Imputation for Repeated Bounded Outcome Data: Statistical and Machine-Learning Approaches

Larracoechea

Borges

2021

Mathematics

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“…As it runs and trains multiple models, it then creates a single strong learner by combining weak learners, thus requiring extra computation and running time [54]. The application of AdaBoost on educational datasets found in previous studies highlights the importance of boosting techniques [55,56].…”

Section: Ensemble Methods 1: Boostingmentioning

confidence: 99%

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

et al. 2021

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Electronic learning management systems provide live environments for students and faculty members to connect with their institutional online portals and perform educational activities virtually. Although modern technologies proactively support these online sessions, students’ active participation remains a challenge that has been discussed in previous research. Additionally, one concern for both parents and teachers is how to accurately measure student performance using different attributes collected during online sessions. Therefore, the research idea undertaken in this study is to understand and predict the performance of the students based on features extracted from electronic learning management systems. The dataset chosen in this study belongs to one of the learning management systems providing a number of features predicting student’s performance. The integrated machine learning model proposed in this research can be useful to make proactive and intelligent decisions according to student performance evaluated through the electronic system’s data. The proposed model consists of five traditional machine learning algorithms, which are further enhanced by applying four ensemble techniques: bagging, boosting, stacking, and voting. The overall F1 scores of the single models are as follows: DT (0.675), RF (0.777), GBT (0.714), NB (0.654), and KNN (0.664). The model performance has shown remarkable improvement using ensemble approaches. The stacking model by combining all five classifiers has outperformed and recorded the highest F1 score (0.8195) among other ensemble methods. The integration of the ML models has improved the prediction ratio and performed better than all other ensemble approaches. The proposed model can be useful for predicting student performance and helping educators to make informed decisions by proactively notifying the students.

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“…Classification is the process of assigning items to a predetermined set. The classification process is also referred to as pattern recognition [13]. KNN is the simplest classification algorithm [14].…”

Section: Introductionmentioning

confidence: 99%

Classification of Customer Loans Using Hybrid Data Mining

2022

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At this time, loans are one of the products offered by banks to their customers. BPR is an abbreviation of Bank Perkreditan Rakyat. BPR is one of the banks that provide loans to their customers. The problem that occurs is that the number of loans given to customers is often not on target and does not meet the criteria. We propose a hybrid data mining method which consists of two phases, first, we will cluster the eligibility of customers to be given a loan using the k-means algorithm, second, we will classify the loan amount using data from the clustering of eligible customers using k-nearest neighbors. As a result of this study, we were able to cluster 25 customers into 2 clusters, 10 customers into the "Not Feasible" cluster, 15 customers into the "Feasible" cluster. Then we also succeeded in classifying customers who applied for new loans with occupation is Entrepreneur, salary is ≥ IDR 5000000, loan guarantees Proof of Vehicle Owner, account balance is < IDR 5000000 and family members is ≥ 4. And the results, classified as Loans with a small amount. We obtained the level of validity of the data testing of each input variable to the target variable reached 97.57%.

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Classifying a Lending Portfolio of Loans with Dynamic Updates via a Machine Learning Technique

Cited by 9 publications

References 33 publications

Imputation for Repeated Bounded Outcome Data: Statistical and Machine-Learning Approaches

Imputation for Repeated Bounded Outcome Data: Statistical and Machine-Learning Approaches

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

Classification of Customer Loans Using Hybrid Data Mining

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