Comprehensive analysis for class imbalance data with concept drift using ensemble based classification

In many information system applications, the environment is dynamic and tremendous amount of streaming data is generated. This scenario enforces additional computational demand on the algorithm to process incoming instances incrementally using restricted memory and time compared to static data mining. Moreover, when the streams of data are collected from different sources, it may exhibit concept drift, which means the variation in the distribution of data and it can have a high degree of class imbalance. The problem of class imbalance occurs when there is a much lower number of an example representing one class than those of the other class. Concept drift and imbalanced streaming data are commonly found in real-world applications such as fraud detection, intrusion detection, decision support system and disease prediction. In this paper, the different concept drift detectors and handling approaches are analysed when dealing with imbalance data. A comparative analysis of concept drift is performed on various data sets like SEA synthetic data stream and real world datasets. Massive Online Analysis (MOA) tool is used to make the comparative study about different learners in a concept drifting environment. The performance measure such as Accuracy, Precision, Recall, F1-score and Kappa statistic has been used to evaluate the performance of the various learners on SEA synthetic data stream and real world dataset. Ensemble classifiers and single learners are employed and tested on the data samples of SEA synthetic data stream, electrical and KDD intrusion data set. The ensemble classifiers provide better accuracy when compared to the single classifier and ensemble based methods has shown good performance compared to strong single learners when dealing with concept drift and class imbalance data.

Automated summary

This study analyzes different methods for handling imbalanced data and concept drift, evaluates the performance of various learners on different datasets using performance metrics, and demonstrates that ensemble classifiers perform better than single classifiers when dealing with concept drift and class imbalance data.