Ensemble Learning on Large Scale Financial Imbalanced Data

Sanabila, H. R.; Jatmiko, Wisnu

doi:10.1109/iwbis.2018.8471702

Cited by 16 publications

(7 citation statements)

References 6 publications

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“…Ensemble methods involve 7 studies that are depicted in Table 3. Resampling method (Sanabila and Jatmiko, 2018) were combined SMOTE and ENN to minimize the bias of majority class and less misclassification in the way of classifying and also AMCS (Yijing et al, 2016) method used Naïve Bayes, DT and KNN to choose the best route for different types of data for classifying multiclass imbalanced data. Multiple authors (Popel et al, 2018;Ahmed et al, 2019) suggested that the use of a machine learning model with DT to address the class imbalance in the field of hybrid methods which denotes in Table 4.…”

Section: Discussion Of Surveyed Workmentioning

confidence: 99%

“…Some medical datasets such as Ovarian I and Ovarian II (Yu and Ni, 2014) datasets are used as well as Breast Cancer, ILPD, Pima Indians, Fertility, Haberman form medical dataset (Junsomboon and Phienthrakul, 2017) are also used. On the other hand, customer behavior of banking transaction Data (Sanabila and Jatmiko, 2018) is used to get the accuracy.…”

Section: Used Dataset In Researchesmentioning

confidence: 99%

“…Total number of research papers for the standards of method designJedrzejowicz et al, 2018;Maldonado et al, 2014; Fernández et al, level algorithm level) 2017;Ebenuwa et al, 2019;Charte et al, 2019;Koziarski et al, 2018;Barua et al, 2012;Verbiest et al, 2012;He et al, 2008) Ensemble 7Sun et al (2015;Yijing et al, 2016;Wei et al, 2017;Yu and Ni, 2014; Lango and Stefanowski, 2017;Wang, 2019;Sanabila and Jatmiko, 2018) Hybrid 7Ahmed et al (2019;Junsomboon and Phienthrakul, 2017;Elhassan and Aljurf, 2016; Gong and Kim, 2017;Popel et al, 2018;Seiffert et al, 2009;Chawla et al, 2003) …”

mentioning

confidence: 99%

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A Survey of Methods for Managing the Classification and Solution of Data Imbalance Problem

Hasib¹,

Iqbal

Shah³

et al. 2020

Journal of Computer Science

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Section: Discussion Of Surveyed Workmentioning

confidence: 99%

Section: Used Dataset In Researchesmentioning

confidence: 99%

mentioning

confidence: 99%

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A Survey of Methods for Managing the Classification and Solution of Data Imbalance Problem

Hasib¹,

Iqbal

Shah³

et al. 2020

Journal of Computer Science

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“…The oversampling method has been employed to solve the problem of class imbalance. Specific probability distribution functions such as Gamma and Gaussian have been used by [28] to generate new data points. A few researchers have combined CNN and LSTM layers for diagnosis of multi-class arrhythmia to achieve an accuracy of 98%.…”

Section: Rationalementioning

confidence: 99%

A Framework for Segmentation and Classification of Arrhythmia Using Novel Bidirectional LSTM Network

Ratnaparkhi¹,

Deshpande²,

Ghule³

2021

IJCDS

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Segmentation of ECG to obtain significant and relevant features has been a significant and inevitable step in achieving dimensionality reduction in automated heart disease diagnosis systems. Reduction in mortality due to the cardiovascular issues rate can only be achieved through accurate and rapid classification. Nonstationarity and high variability augment the complexity of the detection process in the domains of time and frequency. These difficulties are further enhanced due to imbalanced and vague datasets. In this paper, we propose to use a deep learning module to tackle the imbalance in the datasets by applying recurrent neural networks using long short-term memory layers (LSTM) to classify ECG into two classes. It has been seen that LSTM networks can effectively extract sequential timing information in the input ECG samples. To remove the imbalance in the datasets, oversampling and focal lossbased weight balancing techniques have been used, which eventually enhance the accuracy of classification. The proposed approach, an LSTM network with oversampling technique, provides an accuracy of 99.54%, which is considerably better than the traditional approaches that yield an accuracy of around 98%. Moreover, this method is insensitive to the quality of the ECG signal due to the fuzzification process followed in the initial stages of processing the dataset. Deployment of the proposed method for bio-signal telemetry or pharmaceutical research to assist physicians in their work is the most promising advancement in this domain.

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“…Se han usado diversas técnicas de inteligencia artificial sobre conjuntos de datos generados a partir de entidades financieras [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. Los modelos propuestos se centran en dos enfoques relacionados con el área del riesgo crediticio.…”

Section: Introductionunclassified

Predicción de riesgo crediticio en Colombia usando técnicas de inteligencia artificial

Borrero-Tigreros¹,

Bedoya²

2020

revuin

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En este artículo se proponen modelos para la predicción de riesgo crediticio en Colombia utilizando diferentes técnicas de inteligencia artificial. Estos modelos se pueden usar como apoyo por el área de gestión de riesgo en los bancos y tienen como objetivo identificar clientes que podrían incurrir en un estado de mora generando un posible riesgo de crédito para las entidades financieras. En particular, se proponen modelos basados en tres técnicas de aprendizaje supervisado (redes neuronales, árboles de decisión y máquinas de soporte vectorial) para predecir el próximo pago de la cuota de un cliente a partir de datos básicos de la operación, del cliente y de pagos de cuotas anteriores registradas. De acuerdo con los resultados obtenidos, los árboles de decisión resultan ser más exactos que las otras técnicas utilizadas para la predicción de riesgo crediticio conun áreabajo la curva ROC de 88.29%. Los modelos propuestosalcanzan exactitudes similares y en algunos casos superan las exactitudes reportadas en algunos trabajos del estado del arte.

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Ensemble Learning on Large Scale Financial Imbalanced Data

Cited by 16 publications

References 6 publications

A Survey of Methods for Managing the Classification and Solution of Data Imbalance Problem

A Survey of Methods for Managing the Classification and Solution of Data Imbalance Problem

A Framework for Segmentation and Classification of Arrhythmia Using Novel Bidirectional LSTM Network

Predicción de riesgo crediticio en Colombia usando técnicas de inteligencia artificial

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