An Enhanced Comparative Assessment of Ensemble Learning for Credit Scoring

Tounsi, Youssef; Hassouni, Larbi; Anoun, Houda

doi:10.6025/jic/2019/10/1/15-33

Cited by 5 publications

(3 citation statements)

References 28 publications

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“…The sequence of work of all the base classifiers and the method for individual decision combinations are important when building an ensemble classifier. The three major ensemble models-bagging, boosting, and stacking-are presented in Figure 4 [29].…”

Section: ) Single Classifier Modelsmentioning

confidence: 99%

“…There are many combination strategies to construct the ensemble classifier including voting and stacking. Given the fact that ensemble classifiers outperform single classifiers, various theoretical and empirical studies recommend using ensemble classifiers as an effective model because they can improve the prediction accuracy for the following multiple reasons [28], [29]. (1) The ensemble classifier provides a handy solution by training different basic classifiers with different data partitions and then combines their outputs.…”

Section: Introductionmentioning

confidence: 99%

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Intensive Care Unit Mortality Prediction: An Improved Patient-Specific Stacking Ensemble Model

et al. 2020

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The intensive care unit (ICU) admits the most seriously ill patients requiring extensive monitoring. Early ICU mortality prediction is crucial for identifying patients who are at great risk of dying and for providing suitable interventions to save their lives. Accordingly, early prediction of patients at high mortality risk will enable their provision of appropriate and timely medical services. Although various severity scores and machine-learning models have recently been developed for early mortality prediction, such prediction remains challenging. This paper proposes a novel stacking ensemble approach to predict the mortality of ICU patients. Our approach is more accurate and medically intuitive compared to the literature work. Data were prepared and feature selection was processed under the supervision of the ICU domain expert. The data were split into six modalities based on the expert's decisions. For the prediction process, a separate classifier was selected for each modality based on the performance of the classifiers. We utilized the most popular and diverse classifiers in the literature, including linear discriminant analysis, decision tree (DT), multilayer perceptron, k-nearest neighbor, and logistic regression (LR). Then, a stacking ensemble classifier was constructed and optimized based on the fusion of these five classifier decisions. The framework was evaluated using 10,664 patients from the medical information mart for intensive care (MIMIC III) benchmark dataset. To predict patient mortality, extensive experiments were conducted using the patients' time series data of different lengths. For each patient, the first 6, 12, and 24 hours of the first stay were tested. The results indicate that our model outperformed the state-of-the-art approaches in terms of accuracy (94.4%), F1 score (93.7%), precision (96.4%), recall (91.1%), and area under the receiver operator characteristic (ROC) curve (93.3%). These results demonstrate the ability and efficiency of our approach to predict ICU mortality.

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Section: ) Single Classifier Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intensive Care Unit Mortality Prediction: An Improved Patient-Specific Stacking Ensemble Model

et al. 2020

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“…First, bagging considers a homogenous classifier in which learning occurs independently (parallel approach). Then, the results are combined using the averaging process as in the following equation E= (Σ eᵢ)/n, where E refers to the final classifier, and e indicates the base classifier (i.e., RF) [42].…”

Section: B Machine Learning 1) Single Machine Learningmentioning

confidence: 99%

Towards an Accurate Breast Cancer Classification Model based on Ensemble Learning

Hesham¹,

El-Rashidy²,

Rezk³

et al. 2022

IJACSA

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Breast cancer (BC) is considered the most common cancer among women and the major reason for the increased death rate. This condition begins in breast cells and may spread to the rest of the body tissues. The early detection and prediction of BC can help in saving a patient's life. In the last decades, machine learning (ML) has played a significant role in the development of models that can be used to detect and predict various diseases at an early stage, which can greatly increase the survival rate of patients. The importance of ML Classification is attributed to its capability to learn from previous datasets, detects patterns that are difficult to comprehend in massive datasets, predicts a categorical variable within a predefined example and provide accurate results within a short amount of time. Feature selection (FS) method was used to reduce the data dimensionality and choose the optimal feature set. In this paper, we proposed a stacking ensemble model that can differentiate between malignant and benign BC cells. A total of 25 different experiments have been conducted using several classifiers, including logistic regression (LR), decision tree (DT), linear discriminant analysis (LDA), K-nearest neighbor (KNN), naive Bayes (NB), and support vector machine (SVM). In addition to several ensembles, the classifiers included random forest (RF), bagging, AdaBoost, voting, and stacking. The results indicate that our ensemble model outperformed other state-of-the-art models in terms of accuracy (98.6%), precision (89.7%), recall, and F1 score (93.33%). The result shows that the ensemble methods with FS have a high improvement of classification accuracy rather than a single method in detecting BC accurately.

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An Investigation on Vietnamese Credit Scoring Based on Big Data Platform and Ensemble Learning

Tran

Duong

Lam

et al. 2022

Lecture Notes on Data Engineering and Communications Technologies

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An Enhanced Comparative Assessment of Ensemble Learning for Credit Scoring

Cited by 5 publications

References 28 publications

Intensive Care Unit Mortality Prediction: An Improved Patient-Specific Stacking Ensemble Model

Intensive Care Unit Mortality Prediction: An Improved Patient-Specific Stacking Ensemble Model

Towards an Accurate Breast Cancer Classification Model based on Ensemble Learning

An Investigation on Vietnamese Credit Scoring Based on Big Data Platform and Ensemble Learning

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