Online Class Imbalance Learning Research Articles

An extensive study of C-SMOTE, a Continuous Synthetic Minority Oversampling Technique for Evolving Data Streams

Streaming Machine Learning (SML) studies algorithms that update their models, given an unbounded and often non-stationary flow of data performing a single pass. Online class imbalance learning is a branch of SML that combines the challenges of both class imbalance and concept drift. In this paper, we investigate the binary classification problem by rebalancing an imbalanced stream of data in the presence of concept drift, accessing one sample at a time. We propose an extensive comparative study of Continuous Synthetic Minority Oversampling Technique (C-SMOTE), inspired by the popular sampling technique Smote, as a meta-strategy to pipeline with SML classification algorithms. We benchmark C-SMOTE pipelines on both synthetic and real data streams, containing different types of concept drifts, different imbalance levels, and different class distributions. We bring statistical evidence that models learnt with C-SMOTE pipelines improve the minority class performance concerning both the baseline models and the state-of-the-art methods. We also perform a sensitivity analysis to detect the C-SMOTE impact on the majority class performance for the three types of concept drift and several class distributions. Moreover, we show a computational cost analysis in terms of time and memory consumption.

VFC-SMOTE: very fast continuous synthetic minority oversampling for evolving data streams

The world is constantly changing, and so are the massive amount of data produced. However, only a few studies deal with online class imbalance learning that combines the challenges of class-imbalanced data streams and concept drift. In this paper, we propose the very fast continuous synthetic minority oversampling technique (VFC-SMOTE). It is a novel meta-strategy to be prepended to any streaming machine learning classification algorithm aiming at oversampling the minority class using a new version of Smote and Borderline-Smote inspired by Data Sketching. We benchmarked VFC-SMOTE pipelines on synthetic and real data streams containing different concept drifts, imbalance levels, and class distributions. We bring statistical evidence that VFC-SMOTE pipelines learn models whose minority class performances are better than state-of-the-art. Moreover, we analyze the time/memory consumption and the concept drift recovery speed.

Online Learning Method for Drift and Imbalance Problem in Client Credit Assessment

Machine learning algorithms have been widely used in the field of client credit assessment. However, few of the algorithms have focused on and solved the problems of concept drift and class imbalance. Due to changes in the macroeconomic environment and markets, the relationship between client characteristics and credit assessment results may change over time, causing concept drift in client credit assessments. Moreover, client credit assessment data are naturally asymmetric and class imbalanced because of the screening of clients. Aiming at solving the joint research issue of concept drift and class imbalance in client credit assessments, in this paper, a novel sample-based online learning ensemble (SOLE) for client credit assessment is proposed. A novel multiple time scale ensemble classifier and a novel sample-based online class imbalance learning procedure are proposed to handle the potential concept drift and class imbalance in the client credit assessment data streams. The experiments are carried out on two real-world client credit assessment cases, which present a comprehensive comparison between the proposed SOLE and other state-of-the-art online learning algorithms. In addition, the base classifier preference and the computing resource consumption of all the comparative algorithms are tested. In general, SOLE achieves a better performance than other methods using fewer computing resources. In addition, the results of the credit scoring model and the Kolmogorov–Smirnov (KS) test also prove that SOLE has good practicality in actual client credit assessment applications.

A Systematic Study of Online Class Imbalance Learning With Concept Drift.

As an emerging research topic, online class imbalance learning often combines the challenges of both class imbalance and concept drift. It deals with data streams having very skewed class distributions, where concept drift may occur. It has recently received increased research attention; however, very little work addresses the combined problem where both class imbalance and concept drift coexist. As the first systematic study of handling concept drift in class-imbalanced data streams, this paper first provides a comprehensive review of current research progress in this field, including current research focuses and open challenges. Then, an in-depth experimental study is performed, with the goal of understanding how to best overcome concept drift in online learning with class imbalance.

Kernel based online learning for imbalance multiclass classification

In this paper, we propose a weighted online sequential extreme learning machine with kernels (WOS-ELMK) for class imbalance learning (CIL). The existing online sequential extreme learning machine (OS-ELM) methods for CIL use random feature mapping. WOS-ELMK is the first OS-ELM method which uses kernel mapping for online class imbalance learning. The kernel mapping avoids the non-optimal hidden node problem associated with weighted OS-ELM (WOS-ELM) and other existing OS-ELM methods for CIL. WOS-ELMK tackles both the binary class and multiclass imbalance problems in one-by-one as well as chunk-by-chunk learning modes. For imbalanced big data streams, a fixed size window scheme is also implemented for WOS-ELMK. We empirically show that WOS-ELMK obtains superior performance in general than some recently proposed CIL approaches on 17 binary class and 8 multiclass imbalanced datasets.

Resampling-Based Ensemble Methods for Online Class Imbalance Learning

Online class imbalance learning is a new learning problem that combines the challenges of both online learning and class imbalance learning. It deals with data streams having very skewed class distributions. This type of problems commonly exists in real-world applications, such as fault diagnosis of real-time control monitoring systems and intrusion detection in computer networks. In our earlier work, we defined class imbalance online, and proposed two learning algorithms OOB and UOB that build an ensemble model overcoming class imbalance in real time through resampling and time-decayed metrics. In this paper, we further improve the resampling strategy inside OOB and UOB, and look into their performance in both static and dynamic data streams. We give the first comprehensive analysis of class imbalance in data streams, in terms of data distributions, imbalance rates and changes in class imbalance status. We find that UOB is better at recognizing minority-class examples in static data streams, and OOB is more robust against dynamic changes in class imbalance status. The data distribution is a major factor affecting their performance. Based on the insight gained, we then propose two new ensemble methods that maintain both OOB and UOB with adaptive weights for final predictions, called WEOB1 and WEOB2. They are shown to possess the strength of OOB and UOB with good accuracy and robustness.

ONLINE CLASS IMBALANCE LEARNING AND ITS APPLICATIONS IN FAULT DETECTION

Although class imbalance learning and online learning have been extensively studied in the literature separately, online class imbalance learning that considers the challenges of both fields has not drawn much attention. It deals with data streams having very skewed class distributions, such as fault diagnosis of real-time control monitoring systems and intrusion detection in computer networks. To fill in this research gap and contribute to a wide range of real-world applications, this paper first formulates online class imbalance learning problems. Based on the problem formulation, a new online learning algorithm, sampling-based online bagging (SOB), is proposed to tackle class imbalance adaptively. Then, we study how SOB and other state-of-the-art methods can benefit a class of fault detection data under various scenarios and analyze their performance in depth. Through extensive experiments, we find that SOB can balance the performance between classes very well across different data domains and produce stable G-mean when learning constantly imbalanced data streams, but it is sensitive to sudden changes in class imbalance, in which case SOB's predecessor undersampling-based online bagging (UOB) is more robust.