An overview on concept drift learning
AS Iwashita, JP Papa - IEEE access, 2018 - ieeexplore.ieee.org
Concept drift techniques aim at learning patterns from data streams that may change over
time. Although such behavior is not usually expected in controlled environments, real-world …
time. Although such behavior is not usually expected in controlled environments, real-world …
[HTML][HTML] Data stream mining: methods and challenges for handling concept drift
Mining and analysing streaming data is crucial for many applications, and this area of
research has gained extensive attention over the past decade. However, there are several …
research has gained extensive attention over the past decade. However, there are several …
Incremental learning of concept drift from streaming imbalanced data
Learning in nonstationary environments, also known as learning concept drift, is concerned
with learning from data whose statistical characteristics change over time. Concept drift is …
with learning from data whose statistical characteristics change over time. Concept drift is …
Credit card fraud detection and concept-drift adaptation with delayed supervised information
Most fraud-detection systems (FDSs) monitor streams of credit card transactions by means of
classifiers returning alerts for the riskiest payments. Fraud detection is notably a challenging …
classifiers returning alerts for the riskiest payments. Fraud detection is notably a challenging …
Detecting credit card fraud using selected machine learning algorithms
M Puh, L Brkić - 2019 42nd International Convention on …, 2019 - ieeexplore.ieee.org
Due to the immense growth of e-commerce and increased online based payment
possibilities, credit card fraud has become deeply relevant global issue. Recently, there has …
possibilities, credit card fraud has become deeply relevant global issue. Recently, there has …
Concept drift detection and adaption in big imbalance industrial IoT data using an ensemble learning method of offline classifiers
In a smart factory, thousands of industrial Internet of Things (IIoT) devices or sensors are
installed in production machines to collect big data on machine conditions and transmit it to …
installed in production machines to collect big data on machine conditions and transmit it to …
Integration of feature vector selection and support vector machine for classification of imbalanced data
Abstract Support Vector Machine (SVM) has been widely developed for tackling
classification problems. Imbalanced data exist in many practical classification problems …
classification problems. Imbalanced data exist in many practical classification problems …
Online neural network model for non-stationary and imbalanced data stream classification
Abstract “Concept drift” and class imbalance are two challenges for supervised
classifiers.“Concept drift”(or non-stationarity) is changes in the underlying function being …
classifiers.“Concept drift”(or non-stationarity) is changes in the underlying function being …
Ensemble of online neural networks for non-stationary and imbalanced data streams
Abstract Concept drift (non-stationarity) and class imbalance are two important challenges
for supervised classifiers.“Concept drift”(or non-stationarity) refers to changes in the …
for supervised classifiers.“Concept drift”(or non-stationarity) refers to changes in the …
Pro-IDD: Pareto-based ensemble for imbalanced and drifting data streams
Abstract Concept drifts and class imbalance are two primary challenges in supervised data
stream classification, whereas their co-occurrence presents a more complicated learning …
stream classification, whereas their co-occurrence presents a more complicated learning …