Learning on the data stream with nonstationary and imbalanced property is an interesting and complicated problem in data mining as change in class distribution may result in class unbalancing. Many real time problems like intrusion detection, credit card fraud detection, weather forecasting and many more applications suffer concept drift as well as class imbalance as they change with time. The rationale of this paper is to present an effective learning for nonstationary imbalanced data stream which emphasis on misclassified examples with the focus on two-class problems. At the end of paper, proposed algorithms is compared with existing similar approaches using various evaluation metrics
In many applications of information systems learning algorithms have to act in dynamic environments ...
An enormous and ever-growing volume of data is nowadays becoming available in a sequential fashion i...
Machine learning in real-world scenarios is often challenged by concept drift and class imbalance. T...
Learning on the data stream with nonstationary and imbalanced property is an interesting and complic...
Learning patterns from evolving data streams is challenging due to the characteristics of such strea...
Incremental Learning on non stationary distribution has been shown to be a very challenging problem ...
Abstract—Learning in nonstationary environments, also known as learning concept drift, is concerned ...
Data stream classification is the process of learning supervised models from continuous labelled exa...
Streaming data incorporates dynamicity due to a nonstationary environment where data samples may end...
In many applications of information systems learning algorithms have to act in dynamic environments ...
Online class imbalance learning is a new learning problem that combines the challenges of both onlin...
The first book of its kind to review the current status and future direction of the exciting new bra...
In real-world applications, the process generating the data might suffer from nonstationary effects ...
Imbalanced data is ubiquitous in many real-world domains such as bioinformatics, call logs, cancer d...
AbstractThe problem addressed in this paper concerns mining data streams with concept drift. The goa...
In many applications of information systems learning algorithms have to act in dynamic environments ...
An enormous and ever-growing volume of data is nowadays becoming available in a sequential fashion i...
Machine learning in real-world scenarios is often challenged by concept drift and class imbalance. T...
Learning on the data stream with nonstationary and imbalanced property is an interesting and complic...
Learning patterns from evolving data streams is challenging due to the characteristics of such strea...
Incremental Learning on non stationary distribution has been shown to be a very challenging problem ...
Abstract—Learning in nonstationary environments, also known as learning concept drift, is concerned ...
Data stream classification is the process of learning supervised models from continuous labelled exa...
Streaming data incorporates dynamicity due to a nonstationary environment where data samples may end...
In many applications of information systems learning algorithms have to act in dynamic environments ...
Online class imbalance learning is a new learning problem that combines the challenges of both onlin...
The first book of its kind to review the current status and future direction of the exciting new bra...
In real-world applications, the process generating the data might suffer from nonstationary effects ...
Imbalanced data is ubiquitous in many real-world domains such as bioinformatics, call logs, cancer d...
AbstractThe problem addressed in this paper concerns mining data streams with concept drift. The goa...
In many applications of information systems learning algorithms have to act in dynamic environments ...
An enormous and ever-growing volume of data is nowadays becoming available in a sequential fashion i...
Machine learning in real-world scenarios is often challenged by concept drift and class imbalance. T...