[HTML][HTML] Class imbalance should not throw you off balance: Choosing the right classifiers and performance metrics for brain decoding with imbalanced data
Abstract Machine learning (ML) is increasingly used in cognitive, computational and clinical
neuroscience. The reliable and efficient application of ML requires a sound understanding of …
neuroscience. The reliable and efficient application of ML requires a sound understanding of …
Parallel and incremental credit card fraud detection model to handle concept drift and data imbalance
A Somasundaram, S Reddy - Neural Computing and Applications, 2019 - Springer
Real-time fraud detection in credit card transactions is challenging due to the intrinsic
properties of transaction data, namely data imbalance, noise, borderline entities and …
properties of transaction data, namely data imbalance, noise, borderline entities and …
Cost-sensitive Risk Induced Bayesian Inference Bagging (RIBIB) for credit card fraud detection
Credit card fraud represents one of the biggest threats for organizations due to the
probability of huge losses associated with them. This paper presents a cost-sensitive Risk …
probability of huge losses associated with them. This paper presents a cost-sensitive Risk …
A 1D-SP-Net to Determine Early Drought Stress Status of Tomato (Solanum lycopersicum) with Imbalanced Vis/NIR Spectroscopy Data
Detection of the early stages of stress is crucial in stabilizing crop yields and agricultural
production. The aim of this study was to construct a nondestructive and robust method to …
production. The aim of this study was to construct a nondestructive and robust method to …
Real time credit card fraud detection on huge imbalanced data using meta-classifiers
M Kavitha, M Suriakala - 2017 international conference on …, 2017 - ieeexplore.ieee.org
Fraud detection in credit card transactions has several major challenges including the huge
volume and high velocity of the transactions, data imbalance and frequent change in the …
volume and high velocity of the transactions, data imbalance and frequent change in the …
A density weighted fuzzy outlier clustering approach for class imbalanced learning
X Wang, H Wang, Y Wang - Neural Computing and Applications, 2020 - Springer
The class imbalance problem is widely studied in the machine learning community, and it is
present in many real-world applications such as spam filtering, anomaly detection and …
present in many real-world applications such as spam filtering, anomaly detection and …
A novel approach to solve class imbalance problem using noise filter method
Today's one of the popular pre-processing technique in handling class imbalance problems
is over-sampling. It balances the datasets to achieve a high classification rate and also …
is over-sampling. It balances the datasets to achieve a high classification rate and also …
Credit card fraud detection using non-overlapped risk based bagging ensemble (NRBE)
Fraud due to credit card misuse costs consumers several billions of dollars annually. This is
due to the huge usage levels and inability of the systems to automatically detect the …
due to the huge usage levels and inability of the systems to automatically detect the …
Handling data imbalance using a heterogeneous bagging-based stacked ensemble (HBSE) for credit card fraud detection
V Sobanadevi, G Ravi - Intelligence in Big Data Technologies—Beyond …, 2021 - Springer
Increase in electronic payments has lured fraudsters into this domain, leading to reduced
security for customers. Although banks continuously strive to provide enhanced security for …
security for customers. Although banks continuously strive to provide enhanced security for …
Intrusion detection using attribute subset selector Bagging (ASUB) to handle imbalance and noise
Network intrusion detection is becoming an increasing necessity for both organizations and
individuals alike. Detecting intrusions is one of the major components that aims to prevent …
individuals alike. Detecting intrusions is one of the major components that aims to prevent …