Survey on synthetic data generation, evaluation methods and GANs
A Figueira, B Vaz - Mathematics, 2022 - mdpi.com
Synthetic data consists of artificially generated data. When data are scarce, or of poor
quality, synthetic data can be used, for example, to improve the performance of machine …
quality, synthetic data can be used, for example, to improve the performance of machine …
One-class support vector classifiers: A survey
S Alam, SK Sonbhadra, S Agarwal… - Knowledge-Based …, 2020 - Elsevier
Over the past two decades, one-class classification (OCC) becomes very popular due to its
diversified applicability in data mining and pattern recognition problems. Concerning to …
diversified applicability in data mining and pattern recognition problems. Concerning to …
A benchmark of machine learning approaches for credit score prediction
Credit risk assessment plays a key role for correctly supporting financial institutes in defining
their bank policies and commercial strategies. Over the last decade, the emerging of social …
their bank policies and commercial strategies. Over the last decade, the emerging of social …
On the class overlap problem in imbalanced data classification
Class imbalance is an active research area in the machine learning community. However,
existing and recent literature showed that class overlap had a higher negative impact on the …
existing and recent literature showed that class overlap had a higher negative impact on the …
Intelligent fault diagnosis of rolling bearings based on normalized CNN considering data imbalance and variable working conditions
Intelligent fault detection and diagnosis, as an important approach, play a crucial role in
ensuring the stable, reliable and safe operation of rolling bearings, which is one of the most …
ensuring the stable, reliable and safe operation of rolling bearings, which is one of the most …
Class-imbalanced dynamic financial distress prediction based on Adaboost-SVM ensemble combined with SMOTE and time weighting
This paper focuses on how to effectively construct dynamic financial distress prediction
models based on class-imbalanced data streams. Two class-imbalanced dynamic financial …
models based on class-imbalanced data streams. Two class-imbalanced dynamic financial …
A new deep learning ensemble credit risk evaluation model with an improved synthetic minority oversampling technique
F Shen, X Zhao, G Kou, FE Alsaadi - Applied Soft Computing, 2021 - Elsevier
In recent years, research has found that in many credit risk evaluation domains, deep
learning is superior to traditional machine learning methods and classifier ensembles …
learning is superior to traditional machine learning methods and classifier ensembles …
[HTML][HTML] A hybrid sampling algorithm combining M-SMOTE and ENN based on Random forest for medical imbalanced data
The problem of imbalanced data classification often exists in medical diagnosis. Traditional
classification algorithms usually assume that the number of samples in each class is similar …
classification algorithms usually assume that the number of samples in each class is similar …
A cluster-based oversampling algorithm combining SMOTE and k-means for imbalanced medical data
The algorithm of C4. 5 decision tree has the advantages of high classification accuracy, fast
calculation speed and comprehensible classification rules, so it is widely used for medical …
calculation speed and comprehensible classification rules, so it is widely used for medical …
I-SiamIDS: an improved Siam-IDS for handling class imbalance in network-based intrusion detection systems
Abstract Network-based Intrusion Detection Systems (NIDSs) identify malicious activities by
analyzing network traffic. NIDSs are trained with the samples of benign and intrusive …
analyzing network traffic. NIDSs are trained with the samples of benign and intrusive …