Online class imbalance learning deals with data streams that are affected by both concept drift and class imbalance. Online learning tries to find a trade-off between exploiting previously learned information and incorporating new information into the model. This requires both the incremental update of the model and the ability to unlearn outdated information. The improper use of unlearning, however, can lead to the retroactive interference problem, a phenomenon that occurs when newly learned information interferes with the old information and impedes the recall of previously learned information. The problem becomes more severe when the classes are not equally represented, resulting in the removal of minority information from the model. In this work, we propose the Drift-Aware Multi-Memory Model (DAM3), which addresses the class imbalance problem in online learning for memory-based models. DAM3 mitigates class imbalance by incorporating an imbalance-sensitive drift detector, preserving a balanced representation of classes in the model, and resolving retroactive interference using a working memory that prevents the forgetting of old information. We show through experiments on real-world and synthetic datasets that the proposed method mitigates class imbalance and outperforms the state-of-the-art methods.
Resilient Class-Incremental Learning: on the Interplay of Drifting, Unlabelled and Imbalanced Data Streams
KIOS Research and Innovation Center of Excellence, University of Cyprus（希腊塞浦路斯大学KIOS研究中心）；Department of Electrical and Computer Engineering, University of Cyprus（塞浦路斯大学电气与计算机工程系）
In recent years, with the increasing popularity of "Smart Technology", the number of Internet of Things (IoT) devices and systems have surged significantly. Various IoT services and functionalities are based on the analytics of IoT streaming data. However, IoT data analytics faces concept drift challenges due to the dynamic nature of IoT systems and the ever-changing patterns of IoT data streams. In this article, we propose an adaptive IoT streaming data analytics framework for anomaly detection use cases based on optimized LightGBM and concept drift adaptation. A novel drift adaptation method…
While both cost-sensitive learning and online learning have been studied extensively, the effort in simultaneously dealing with these two issues is limited. Aiming at this challenge task, a novel learning framework is proposed in this paper. The key idea is based on the fusion of online ensemble algorithms and the state of the art batch mode cost-sensitive bagging/boosting algorithms. Within this framework, two separately developed research areas are bridged together, and a batch of theoretically sound online cost-sensitive bagging and online cost-sensitive boosting algorithms are first propos…
As the number of Internet of Things (IoT) devices and systems have surged, IoT data analytics techniques have been developed to detect malicious cyber-attacks and secure IoT systems; however, concept drift issues often occur in IoT data analytics, as IoT data is often dynamic data streams that change over time, causing model degradation and attack detection failure. This is because traditional data analytics models are static models that cannot adapt to data distribution changes. In this paper, we propose a Performance Weighted Probability Averaging Ensemble (PWPAE) framework for drift adaptiv…
Extension OL-MDISF: Online Learning from Mix-Typed, Drifted, and Incomplete Streaming Features
College of Cyber Security, Jinan University, Guangzhou, Guangdong（广州大学网络安全学院）；College of Computer and Information Science, Southwest University, Chongqing（西南大学计算机与信息科学学院）；Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, Anhui（大数据知识工程重点实验室（教育部））
The performance of classification algorithms with a massive and highly imbalanced data stream depends upon efficient balancing strategy. Some techniques of balancing strategy have been applied in the past with Batch data to resolve the class imbalance problem. This paper proposes a new incremental data balancing framework which can work with massive imbalanced data streams. In this paper, we choose Racing Algorithm as an automated data balancing technique which optimizes the balancing techniques. We applied Random Forest classification algorithm which can deal with the massive data stream. We …
Turning Drift into Constraint: Robust Reasoning Alignment in Non-Stationary Multi-Stream Environments
Australian Artificial Intelligence Institute (AAII)（澳大利亚人工智能研究所）；Faulty of Engineering and Information Technology（工程与信息技术学院）；University of Technology Sydney（悉尼技术大学）；Australia（澳大利亚）
Concept drift refers to changes in the distribution of underlying data and is an inherent property of evolving data streams. Ensemble learning, with dynamic classifiers, has proved to be an efficient method of handling concept drift. However, the best way to create and maintain ensemble diversity with evolving streams is still a challenging problem. In contrast to estimating diversity via inputs, outputs, or classifier parameters, we propose a diversity measurement based on whether the ensemble members agree on the probability of a regional distribution change. In our method, estimations over …
Learning Unbiased Cluster Descriptors for Interpretable Imbalanced Concept Drift Detection
School of Computer Science and Technology, Guangdong University of Technology（广东工业大学计算机科学与技术学院）；Department of Computer Science, Hong Kong Baptist University（香港 Baptist 大学计算机科学系）；Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（中国教育部多媒体可信感知与高效计算重点实验室，厦门大学）；School of Mathematics and Statistics, Guangdong University of Technology（广东工业大学数学与统计学院）
Concept drift is formally defined as the change in joint distribution of a set of input variables X and a target variable y. The two types of drift that are extensively studied are real drift and virtual drift where the former is the change in posterior probabilities p(y|X) while the latter is the change in distribution of X without affecting the posterior probabilities. Many approaches on concept drift detection either assume full availability of data labels, y or handle only the virtual drift. In a streaming environment, the assumption of full availability of data labels, y is questioned. On…
Neural Processing Research Center, Seoul National University（首尔大学神经处理研究中心）
Continual learning from a sequential stream of data is a crucial challenge for machine learning research. Most studies have been conducted on this topic under the single-label classification setting along with an assumption of balanced label distribution. This work expands this research horizon towards multi-label classification. In doing so, we identify unanticipated adversity innately existent in many multi-label datasets, the long-tailed distribution. We jointly address the two independently solved problems, Catastropic Forgetting and the long-tailed label distribution by first empirically …
A Framework for Evaluating and Benchmarking Concept Drift Detection Methods
University of Coimbra（科英布拉大学）；Victoria University of Wellington（惠灵顿维多利亚大学）；Commerzbank（德国商业银行）；University of Waikato（怀卡托大学）；AI Institute, University of Waikato（怀卡托大学人工智能研究所）
In the fields of big data, AI, and streaming processing, we work with large amounts of data from multiple sources. Due to memory and network limitations, we process data streams on distributed systems to alleviate computational and network loads. When data streams with non-uniform distributions are processed, we often observe overloaded partitions due to the use of simple hash partitioning. To tackle this imbalance, we can use dynamic partitioning algorithms that require a sampling algorithm to precisely estimate the underlying distribution of the data stream. There is no standardized way to t…
Poznan University of Technology（波兹南理工大学）；Institute of Computing Science（计算科学研究所）
This work is aimed at the experimental studying the influence of local data characteristics and drifts on the difficulties of learning various online classifiers from multi-class imbalanced data streams. Firstly we present a categorization of these data factors and drifts in the context of imbalanced streams, then we introduce the generators of synthetic streams that model these factors and drifts. The results of many experiments with synthetically generated data streams have shown a much greater role of the overlapping between many minority classes (the type of borderline examples) than for s…
Continual learning from data streams is among the most important topics in contemporary machine learning. One of the biggest challenges in this domain lies in creating algorithms that can continuously adapt to arriving data. However, previously learned knowledge may become outdated, as streams evolve over time. This phenomenon is known as concept drift and must be detected to facilitate efficient adaptation of the learning model. While there exists a plethora of drift detectors, all of them assume that we are dealing with roughly balanced classes. In the case of imbalanced data streams, those …
Classifiers trained on data sets possessing an imbalanced class distribution are known to exhibit poor generalisation performance. This is known as the imbalanced learning problem. The problem becomes particularly acute when we consider incremental classifiers operating on imbalanced data streams, especially when the learning objective is rare class identification. As accuracy may provide a misleading impression of performance on imbalanced data, existing stream classifiers based on accuracy can suffer poor minority class performance on imbalanced streams, with the result being low minority cl…
Harmonized Gradient Descent for Class Imbalanced Data Stream Online Learning
Peng Cheng Laboratory（鹏城实验室）；School of Automation, Chongqing University（重庆大学自动化学院）；School of Information Science and Engineering, East China University of Science and Technology（东华大学信息科学与工程学院）
L3S Research Center（L3S研究中心）；Leibniz University of Hannover（汉诺威莱布尼茨大学）；Carnegie Mellon University（卡内基梅隆大学）
Data-driven learning algorithms are employed in many online applications, in which data become available over time, like network monitoring, stock price prediction, job applications, etc. The underlying data distribution might evolve over time calling for model adaptation as new instances arrive and old instances become obsolete. In such dynamic environments, the so-called data streams, fairness-aware learning cannot be considered as a one-off requirement, but rather it should comprise a continual requirement over the stream. Recent fairness-aware stream classifiers ignore the problem of class…
Learning Self-Growth Maps for Fast and Accurate Imbalanced Streaming Data Clustering
School of Computer Science and Technology, Guangdong University of Technology（广东工业大学计算机科学与技术学院）；Department of Computer Science, Hong Kong Baptist University（香港 Baptist 大学计算机科学系）
Autonomous Drift Learning in Data Streams: A Unified Perspective
Australian Artificial Intelligence Institute (AAII), Faculty of Engineering and Information Technology, University of Technology Sydney（澳大利亚人工智能研究所（AAII）、工程与信息科技学院、悉尼技术大学）
The present study introduces a method for improving the classification performance of imbalanced multiclass data streams from wireless body worn sensors. Data imbalance is an inherent problem in activity recognition caused by the irregular time distribution of activities, which are sequential and dependent on previous movements. We use conditional random fields (CRF), a graphical model for structured classification, to take advantage of dependencies between activities in a sequence. However, CRFs do not consider the negative effects of class imbalance during training. We propose a class-wise d…
A Hybrid Active-Passive Approach to Imbalanced Nonstationary Data Stream Classification
KIOS Research and Innovation Center of Excellence（KIOS研究与创新中心）；Department of Electrical and Computer Engineering（电气与计算机工程系）；University of Cyprus（塞浦路斯大学）
Universidade Federal de Pernambuco（伯南布哥联邦大学）；Pontifícia Universidade Católica do Paraná（巴拉那天主教大学）
Data streams are often defined as large amounts of data flowing continuously at high speed. Moreover, these data are likely subject to changes in data distribution, known as concept drift. Given all the reasons mentioned above, learning from streams is often online and under restrictions of memory consumption and run-time. Although many classification algorithms exist, most of the works published in the area use Naive Bayes (NB) and Hoeffding Trees (HT) as base learners in their experiments. This article proposes an in-depth evaluation of k-Nearest Neighbors (k-NN) as a candidate for classifyi…
The domain of radio astronomy is currently facing significant computational challenges, foremost amongst which are those posed by the development of the world's largest radio telescope, the Square Kilometre Array (SKA). Preliminary specifications for this instrument suggest that the final design will incorporate between 2000 and 3000 individual 15 metre receiving dishes, which together can be expected to produce a data rate of many TB/s. Given such a high data rate, it becomes crucial to consider how this information will be processed and stored to maximise its scientific utility. In this pape…
Generalized Incremental Learning under Concept Drift across Evolving Data Streams
Australian Artificial Intelligence Institute (AAII), \ of Technology Sydney Sydney NSW Australia；Australian Artificial Intelligence Institute (AAII), \ of Technology Sydney
Bilkent University（比尔肯特大学）；Manning College of Information and Computer Sciences（曼宁信息与计算机科学学院）
In a data stream environment, classification models must handle concept drift efficiently and effectively. Ensemble methods are widely used for this purpose; however, the ones available in the literature either use a large data chunk to update the model or learn the data one by one. In the former, the model may miss the changes in the data distribution, and in the latter, the model may suffer from inefficiency and instability. To address these issues, we introduce a novel ensemble approach based on the Broad Learning System (BLS), where mini chunks are used at each update. BLS is an effective …
Wrocław University of Science and Technology（弗罗茨瓦夫科技大学）
stream-learn is a Python package compatible with scikit-learn and developed for the drifting and imbalanced data stream analysis. Its main component is a stream generator, which allows to produce a synthetic data stream that may incorporate each of the three main concept drift types (i.e. sudden, gradual and incremental drift) in their recurring or non-recurring versions. The package allows conducting experiments following established evaluation methodologies (i.e. Test-Then-Train and Prequential). In addition, estimators adapted for data stream classification have been implemented, including …
National University of Singapore（新加坡国立大学）；IIT Bombay（印度理工学院孟买分校）；Harvard University（哈佛大学）
Given a stream of entries over time in a multi-dimensional data setting where concept drift is present, how can we detect anomalous activities? Most of the existing unsupervised anomaly detection approaches seek to detect anomalous events in an offline fashion and require a large amount of data for training. This is not practical in real-life scenarios where we receive the data in a streaming manner and do not know the size of the stream beforehand. Thus, we need a data-efficient method that can detect and adapt to changing data trends, or concept drift, in an online manner. In this work, we p…
Stream classification methods classify a continuous stream of data as new labelled samples arrive. They often also have to deal with concept drift. This paper focuses on seasonal drift in stream classification, which can be found in many real-world application data sources. Traditional approaches of stream classification consider seasonal drift by including seasonal dummy/indicator variables or building separate models for each season. But these approaches have strong limitations in high-dimensional classification problems, or with complex seasonal patterns. This paper explores how to best han…
Dynamically Anchored Prompting for Task-Imbalanced Continual Learning
Xiamen University（厦门大学）；Univ. Grenoble Alpes（格勒诺布尔大学）；Inria（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔理工学院）；LJK（让·昆廷诺实验室）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室（深圳））
TECNALIA, Basque Research and Technology Alliance (BRTA)（泰克纳利亚，巴斯克研究与技术联盟（BRTA））；University of the Basque Country (UPV/EHU)（巴斯克大学（UPV/EHU））
AI-based digital twins are at the leading edge of the Industry 4.0 revolution, which are technologically empowered by the Internet of Things and real-time data analysis. Information collected from industrial assets is produced in a continuous fashion, yielding data streams that must be processed under stringent timing constraints. Such data streams are usually subject to non-stationary phenomena, causing that the data distribution of the streams may change, and thus the knowledge captured by models used for data analysis may become obsolete (leading to the so-called concept drift effect). The …
Indian Institute of Technology Delhi（印度理工学院德里分校）；Indraprastha Institute of Information Technology, Delhi（德里英德拉普拉斯塔信息技术学院）
We propose Enhash, a fast ensemble learner that detects \textit{concept drift} in a data stream. A stream may consist of abrupt, gradual, virtual, or recurring events, or a mixture of various types of drift. Enhash employs projection hash to insert an incoming sample. We show empirically that the proposed method has competitive performance to existing ensemble learners in much lesser time. Also, Enhash has moderate resource requirements. Experiments relevant to performance comparison were performed on 6 artificial and 4 real data sets consisting of various types of drifts.
Classifiers deployed in the real world operate in a dynamic environment, where the data distribution can change over time. These changes, referred to as concept drift, can cause the predictive performance of the classifier to drop over time, thereby making it obsolete. To be of any real use, these classifiers need to detect drifts and be able to adapt to them, over time. Detecting drifts has traditionally been approached as a supervised task, with labeled data constantly being used for validating the learned model. Although effective in detecting drifts, these techniques are impractical, as la…
In big data era, the data continuously generated and its distribution may keep changes overtime. These challenges in online stream of data are known as concept drift. In this paper, we proposed the Adaptive Convolutional ELM method (ACNNELM) as enhancement of Convolutional Neural Network (CNN) with a hybrid Extreme Learning Machine (ELM) model plus adaptive capability. This method is aimed for concept drift handling. We enhanced the CNN as convolutional hiererchical features representation learner combined with Elastic ELM (E$^2$LM) as a parallel supervised classifier. We propose an Adaptive O…
Learning from data streams is an increasingly important topic in data mining, machine learning, and artificial intelligence in general. A major focus in the data stream literature is on designing methods that can deal with concept drift, a challenge where the generating distribution changes over time. A general assumption in most of this literature is that instances are independently distributed in the stream. In this work we show that, in the context of concept drift, this assumption is contradictory, and that the presence of concept drift necessarily implies temporal dependence; and thus som…
