Evolutionary machine learning: A survey
Evolutionary Computation (EC) approaches are inspired by nature and solve optimization
problems in a stochastic manner. They can offer a reliable and effective approach to address …
problems in a stochastic manner. They can offer a reliable and effective approach to address …
Deep learning fault diagnosis method based on global optimization GAN for unbalanced data
F Zhou, S Yang, H Fujita, D Chen, C Wen - Knowledge-Based Systems, 2020 - Elsevier
Deep learning can be applied to the field of fault diagnosis for its powerful feature
representation capabilities. When a certain class fault samples available are very limited, it …
representation capabilities. When a certain class fault samples available are very limited, it …
Multi-class misclassification cost matrix for credit ratings in peer-to-peer lending
Online peer-to-peer (P2P) lending is a new form of loans. Different from traditional banks,
lenders provide loans to borrowers directly through P2P platforms. Since many P2P loans …
lenders provide loans to borrowers directly through P2P platforms. Since many P2P loans …
An improved and random synthetic minority oversampling technique for imbalanced data
G Wei, W Mu, Y Song, J Dou - Knowledge-based systems, 2022 - Elsevier
Imbalanced data learning has become a major challenge in data mining and machine
learning. Oversampling is an effective way to re-achieve the balance by generating new …
learning. Oversampling is an effective way to re-achieve the balance by generating new …
2-stage modified random forest model for credit risk assessment of P2P network lending to “Three Rurals” borrowers
C Rao, M Liu, M Goh, J Wen - Applied Soft Computing, 2020 - Elsevier
With the rapid growth of the P2P online loan industry in the “Three Rurals”(agriculture, rural
areas, and farmers) sector, it is imperative to manage the borrowing risk of borrowers in the …
areas, and farmers) sector, it is imperative to manage the borrowing risk of borrowers in the …
Improved generative adversarial network for vibration-based fault diagnosis with imbalanced data
B Zhao, Q Yuan - Measurement, 2021 - Elsevier
Effective fault diagnosis is essential for maintaining the safe running of machine systems.
Recently, the data-driven methods have shown great potential in intelligent fault diagnosis …
Recently, the data-driven methods have shown great potential in intelligent fault diagnosis …
Class-imbalanced deep learning via a class-balanced ensemble
Class imbalance is a prevalent phenomenon in various real-world applications and it
presents significant challenges to model learning, including deep learning. In this work, we …
presents significant challenges to model learning, including deep learning. In this work, we …
A hybrid data-level ensemble to enable learning from highly imbalanced dataset
Highly imbalanced class distribution has been well-recognized as a major cause of
performance degradation for most supervised learning algorithms. Unfortunately, such …
performance degradation for most supervised learning algorithms. Unfortunately, such …
Entropy-based fuzzy support vector machine for imbalanced datasets
Imbalanced problem occurs when the size of the positive class is much smaller than that of
the negative one. Positive class usually refers to the main interest of the classification task …
the negative one. Positive class usually refers to the main interest of the classification task …
A cost-sensitive deep learning-based approach for network traffic classification
Network traffic classification (NTC) plays an important role in cyber security and network
performance, for example in intrusion detection and facilitating a higher quality of service …
performance, for example in intrusion detection and facilitating a higher quality of service …