Incremental Learning of Concept Drift from Streaming Imbalanced Data (original) (raw)

An Ensemble Based Incremental Learning Framework for Concept Drift and Class Imbalance

2010

Abstract—We have recently introduced an incremental learning algorithm, Learn++. NSE, designed to learn in nonstationary environments, and has been shown to provide an attractive solution to a number of concept drift problems under different drift scenarios. However, Learn++. NSE relies on error to weigh the classifiers in the ensemble on the most recent data. For balanced class distributions, this approach works very well, but when faced with imbalanced data, error is no longer an acceptable measure of performance.

IJERT-Classification and Adaptive Ensemble Models of Concept Drift

International Journal of Engineering Research and Technology (IJERT), 2014

https://www.ijert.org/classification-and-adaptive-ensemble-models-of-concept-drift https://www.ijert.org/research/classification-and-adaptive-ensemble-models-of-concept-drift-IJERTV3IS20629.pdf Mining data streams with concept drifts using ensemble classifiers is a challenging task to cope with special properties of data streams in the field of data mining. The concept drift methods are used to identify classifiers. The accurate drift detection maintains the high performance. This project deals with to identify the concept drift problem. A new algorithm, Accuracy Updated Ensemble (AUE) is proposed which extends the different classifiers such as accuracy weighted ensemble by using classifiers and updating them according to the new arrival of data. In existing, Accuracy Weighted Ensemble (AWE) is used to train a new classifier on each incoming data block and use that block to evaluate all the existing classifiers in the ensemble. The component in the classifiers are weighted by their expected accuracy on the test data, the ensemble improves classification accuracy over a single classifier. Many Experiments have evolved with several data sets, lagging in processing time and memory aspects of mining AUE is more accurate than AWE which provides best average classification accuracy and less memory consuming than other ensemble approaches.

Fast Adapting Ensemble: A New Algorithm for Mining Data Streams with Concept Drift

The Scientific World Journal, 2015

The treatment of large data streams in the presence of concept drifts is one of the main challenges in the field of data mining, particularly when the algorithms have to deal with concepts that disappear and then reappear. This paper presents a new algorithm, called Fast Adapting Ensemble (FAE), which adapts very quickly to both abrupt and gradual concept drifts, and has been specifically designed to deal with recurring concepts. FAE processes the learning examples in blocks of the same size, but it does not have to wait for the batch to be complete in order to adapt its base classification mechanism. FAE incorporates a drift detector to improve the handling of abrupt concept drifts and stores a set of inactive classifiers that represent old concepts, which are activated very quickly when these concepts reappear. We compare our new algorithm with various well-known learning algorithms, taking into account, common benchmark datasets. The experiments show promising results from the pr...

Adaptive online learning for classification under concept drift

International Journal of Computational Science and Engineering, 2021

In machine learning and predictive analytics, the underlying data distributions tend to change with the course of time known as concept drift. Accurate labelling in case of supervised learning algorithms is essential to build consistent ensemble models. However, several real-world applications suffer from drifting data concepts which leads to deterioration in the performance of prediction systems. To tackle these challenges, we study various concept drift handling approaches which identify major types of drift patterns such as abrupt, gradual, and recurring in drifting data streams. This study also highlights the need for adaptive algorithms and demonstrates comparison of various state-of-the-art drift handling techniques by analysing their classification accuracy on artificially generated drifting data streams and real datasets.

Learning from Data Streams with Concept Drift

2008

Summary Increasing access to incredibly large, nonstationary datasets and corresponding demands to analyse these data has led to the development of new online algorithms for performing machine learning on data streams. An important feature of real-world data streams is “concept drift,” whereby the distributions underlying the data can change arbitrarily over time.