Multi-Stream Concept Drift Self-Adaptation Using Graph Neural Network

Spatial and temporal data such as multiple data streams often have concept drift problems, which refers to changes of the data distributions over time. Once concept drift occurs, a stationary machine learning predictor will probably be invalid because the testing data have a different data distribution from that of the training data. Recent studies in data streams aim to address this issue by concept drift adaptation techniques. Concept drift adaptation methods update the predictor by time and have been validated to provide accurate real-time prediction for a single data stream. However, handling multiple relevant data streams is still an unsolved challenge in this area, considering many real-world applications generate multiple data streams that are simultaneously evolving. To fill this gap, we here present a predicting network for multiple data streams, named MuNet. MuNet leverages the dependency between data streams to efficiently lower down the computational cost of real-time predictions for multiple data streams. In MuNet, an online-learned Bayesian neural network (BNN) is designed as a connector between streams. The BNN-connector can continuously use the real-time information from only one base stream to correct the stationary predictor for the other streams, which avoids the high cost caused by repeated adaptation.

Anomaly detection in streaming data is an important task for many real-world applications, such as network security, fraud detection, and system monitoring. However, streaming data often exhibit concept drift, which means that the data distribution changes over time. This poses a significant challenge for many anomaly detection algorithms, as they need to adapt to the evolving data to maintain high detection accuracy. Existing streaming anomaly detection algorithms lack a unified evaluation framework that validly assesses their performance and robustness under different types of concept drifts and anomalies. In this paper, we conduct a systematic technical review of the state-of-the-art methods for anomaly detection in streaming data. We propose a new data generator, called SCAR (Streaming data generator with Customizable Anomalies and concept dRifts), that can synthesize streaming data based on synthetic and real-world datasets from different domains. Furthermore, we adapt four static anomaly detection models to the streaming setting using a generic reconstruction strategy as baselines, and then compare them systematically with 9 existing streaming anomaly detection algorithms on 76 synthesized datasets that have various types of anomalies and concept drifts. The challenges and future research directions for anomaly detection in streaming data are also presented.

Aim/Purpose: This paper describes how to use a multilayer perceptron to improve concept drift recovery in streaming environments. Background: Classifying instances in a data stream environment with concept drift is a challenging topic. The base learner must be adapted online to the current data. Several data mining algorithms have been adapted/used to this type of environment. In this study, two techniques are used to speed up the adaptation of an artificial neural network to the current data, increasing its predictive accuracy while detecting the concept drift sooner. Methodology: Experiments were performed to analyze how some techniques behave in different scenarios and compare them with other classifiers built to deal with data streams and concept drifts. Contribution: This study suggests two techniques to improve the classification results: an embedded concept drift detection method to identify when a change has occurred and setting the learning rate to a higher level whenever a new concept is being learned to give more weight to recent instances, with its value decreased over time. Findings: Results indicate that gradually reducing the learning rate with an embedded concept drift detector has better statistical results than other single classifiers built to deal with data streams and concept drifts. Recommendations for Practitioners: Based on the empirical results, this study provides recommendations on how to improve the multilayer perceptron in data stream environments suffering from concept drifts. Recommendation for Researchers: Researchers should conduct investigations to increase the number of base classifiers used in data stream environments and in situations where concept drifts occur. Impact on Society: The objective of this study is to increase the use of multilayer perceptrons in data stream environments suffering from concept drifts, as nowadays, Hoeffding Trees and Naive Bayes are the base classifiers mostly used. Future Research: Additional research includes adapting the online learning rate by increasing/decreasing it based on the performance of the Multilayer Perceptron. This scheme would allow the removal of parameters that must be set by the user, like learning rate upper bound and number of instances to return to the stable value.

The advances in computing software, hardware, connected devices and wireless communication infrastructure in recent years have led to the desire to work with streaming data sources. Yet the number of techniques, approaches and algorithms which can work with data from a streaming source is still very limited, compared with batched data. Although data mining techniques have been a well-studied topic of knowledge discovery for decades, many unique properties as well as challenges in learning from a data stream have not been considered properly due to the actual presence of and the real needs to mine information from streaming data sources. This thesis aims to contribute to the knowledge by developing a rule-based algorithm to specifically learn classification rules from data streams, with the learned rules are expressive so that a human user can easily interpret the concept and rationale behind the predictions of the created model. There are two main structures to represent a classification model; the ‘tree-based’ structure and the ‘rule-based’ structure. Even though both forms of representation are popular and well-known in traditional data mining, they are different when it comes to interpretability and quality of models in certain circumstances. The first part of this thesis analyses background work and relevant topics in learning classification rules from data streams. This study provides information about the essential requirements to produce high quality classification rules from data streams and how many systems, algorithms and techniques related to learn the classification of a static dataset are not applicable in a streaming environment. The second part of the thesis investigates at a new technique to improve the efficiency and accuracy in learning heuristics from numeric features from a streaming data source. The computational cost is one of the important factors to be considered for an effective and practical learning algorithm/system because of the needs to learn from continuous arrivals of data examples sequentially and discard the seen data examples. If the computing cost is too expensive, then one may not be able to keep pace with the arrival of high velocity and possibly unbound data streams. The proposed technique was first discussed in the context of the use of Gaussian distribution as heuristics for building rule terms on numeric features. Secondly, empirical evaluation shows the successful integration of the proposed technique into an existing rule-based algorithm for the data stream, eRules. Continuing on the topic of a rule-based algorithm for classification data streams, the use of Hoeffding’s Inequality addresses another problem in learning from a data stream, namely how much data should be seen from a data stream before starting learning and how to keep the model updated over time. By incorporating the theory from Hoeffding’s Inequality, this study presents the Hoeffding Rules algorithm, which can induce modular rules directly from a streaming data source with dynamic window sizes throughout the learning period to ensure the efficiency and robustness towards the concept drifts. Concept drift is another unique challenge in mining data streams which the underlying concept of the data can change either gradually or abruptly over time and the learner should adapt to these changes as quickly as possible. This research focuses on the development of a rule-based algorithm, Hoeffding Rules, for data stream which considers streaming environments as primary data sources and addresses several unique challenges in learning rules from data streams such as concept drifts and computational efficiency. This knowledge facilitates the need and the importance of an interpretable machine learning model; applying new studies to improve the ability to mine useful insights from potentially high velocity, high volume and unbounded data streams. More broadly, this research complements the study in learning classification rules from data streams to address some of the unique challenges in data streams compared with conventional batch data, with the knowledge necessary to systematically and effectively learn expressive and modular classification rules from data streams.

Graph Neural Networks (GNNs) have demonstrated significant success in various graph-related tasks. However, their performance is highly dependent on the availability of sufficient, class-balanced annotated data and an accurate graph structure. GNNs are vulnerable to substantial performance degradation when confronted with limited annotated samples, class imbalance, or noisy graph structures. First, a Dual GNN learning framework is introduced for semi-supervised node classification under limited supervision. The framework comprises two GNN-based node prediction modules: a primary module, which uses the input graph structure to induce standard node embeddings and predictions, and an auxiliary module, which leverages spectral clustering to construct a new graph structure and learn new node embeddings and predictions. These modules collaborate to enable end-to-end learning of discriminative node representations. Next, a graph augmentation method called Graph Dual Mixup (GDM) is proposed for graph classification with scarce labels. GDM employs a graph structural auto-encoder to learn structural embeddings, applying mixup in the learned structural embedding space to generate new graph structures. Additionally, mixup is applied to input node features, generating node features for new graph instances. Together, these generated node features and graph structures contribute new graphs that increase the size and diversity of the labeled dataset, improving classification performance. To enhance robustness against adversarial attacks on graph structures, an Efficient Low-Rank GNN is introduced. This approach learns robust low-rank sparse graph structures through a two-stage process. In the first stage, Singular Value Decomposition is used to estimate a low-rank approximation of the graph structure. This estimate is refined in the second stage by jointly learning a low-rank sparse graph structure alongside the GNN model, resulting in enhanced robustness. Finally, to address class-imbalanced node classification, a Unified GNN Learning (Uni-GNN) framework is proposed. Uni-GNN integrates structural and semantic connectivity representations to extend the propagation of embeddings to non-adjacent structural neighbors and semantically similar nodes, facilitating the diffusion of discriminative information. Additionally, it incorporates a balanced pseudo-label generation mechanism to improve the representations of minority classes. Comprehensive evaluations on benchmark datasets demonstrate that these methods outperform state-of-the-art approaches and hold significant potential for advancing GNN learning with limited supervision.

Concept drift referring to the changes of data distributions has been one critical challenge typically associated with mining data streams. Current drift detection and adaptation methods focus on how to immediately detect the distribution changes once the concept drift occurs and swiftly update the model to be applicable to the newly arrived data instances. Most of those methods assume the data does not have noise or the noise is too weak to affect the modeling procedure. However, realworld data are normally contaminated, and denoise techniques are highly preferred as a necessary preprocess. This issue is more complex for a data stream with concept drift because the noise is very likely to be confused with drift. Motivated by that, this paper proposes a Noise-tolerant Fuzzy c-means based drift Adaptation method (NFA) which can adapt to the changing distributions and is suitable for noisy data streams. The concept drift problem is solved by using a fuzzy c-means based regression model to continuously include the most relevant data instances to the latest pattern in the training set. In addition, a denoise technique is designed in NFA to remove noise, and the ability of incremental updating enables it to be embedded in the incremental drift adaptation process, and therefore NFA can solve concept drift and noise problems at the same time. Experimental evaluation results also show good performance of our method on handling data streams with concept drift and noise.

Data stream mining has been receiving increased attention due to its presence in a wide range of applications, such as sensor networks, banking, and telecommunication. One of the most important challenges during learning from data streams is reacting to concept drift, i.e. unforeseen changes of the stream's underlying data distribution. Traditional methods always used online learning to handle the concept drift problem. However, online learning requires high time cost during online training. To overcome this shortcoming, this paper proposes a Kalman filtering approach, which can provide robust concept drift detection, to track concept drift. Once concept drift happens, the online extreme learning machine is applied to update the tracking model, whereas the offline extreme learning machine is used when no concept drift occurs. Based on this idea, we propose a fusion framework to combine online and offline extreme learning machine to efficiently track the data stream. The experiment results indicate the superior performance of our method.

A frequent problem in data stream mining is concept drift, meaning the data distribution changes over time. A common issue when dealing with concept drift is insufficient data. Real-world applications of data stream mining often involve multiple data streams. However, most concept drift methods handle these data streams separately. This study uses data from other data streams to handle the problem of insufficient data. We propose a novel Multistream Concept Drift Handling Framework via data sharing, containing a fuzzy membership-based drift detection (FMDD) component and a fuzzy membership-based drift adaptation (FMDA) component, to train the new learning model for drifting streams by sharing weighted data from other nondrifting streams. A stream fuzzy set is defined with membership functions that measure the degree to which samples belong to a data stream. Our Concept Drift Handling Framework can detect when and in which stream concept drift occurs, and therefore the insufficient data issue can be solved by adding the weighted data from nondrifting streams to train new learning models. Synthetic and real-world experimental results show that our method can help avoid the insufficient data issue and thereby significantly improve the prediction performance.

Semi-supervised classification on data streams with recurring concept drift and concept evolution

Network representation learning is receiving increasing attention from scholars. Among them, methods based on graph neural networks have become particularly popular. However, most existing methods currently only focus on networks with a single type of relations. In the real world, networks contain a wealth of diverse information, with multiple types of relationships between nodes. In this paper, we propose a graph neural network-based multiplex network representation learning model (GNMRL). We model nodes within each layer of the multiplex network by aggregating neighbor information. Additionally, since nodes are influenced by different network layers, we integrate node interaction information across network layers. We conducted systematic experiments on four datasets, and the results show that GNMRL outperforms other comparison methods in both link prediction and node classification tasks.

Recent research shows that rule based models perform well while classifying large data sets such as data streams with concept drifts. A genetic algorithm is a strong rule based classification algorithm which is used only for mining static small data sets. If the genetic algorithm can be made scalable and adaptable by reducing its I/O intensity, it will become an efficient and effective tool for mining large data sets like data streams. In this paper a scalable and adaptable online genetic algorithm is proposed to mine classification rules for the data streams with concept drifts. Since the data streams are generated continuously in a rapid rate, the proposed method does not use a fixed static data set for fitness calculation. Instead, it extracts a small snapshot of the training example from the current part of data stream whenever data is required for the fitness calculation. The proposed method also builds rules for all the classes separately in a parallel independent iterative manner. This makes the proposed method scalable to the data streams and also adaptable to the concept drifts that occur in the data stream in a fast and more natural way without storing the whole stream or a part of the stream in a compressed form as done by the other rule based algorithms. The results of the proposed method are comparable with the other standard methods which are used for mining the data streams.

Long short-term memory self-adapting online random forests for evolving data stream regression

In evolving data stream, when its concept undergoes a change it is known as concept drift. Detecting concept drift and handling it is a challenging task in data stream mining. If an algorithm is not adapted to concept drift, then it directly affects its performance. A number of algorithms have been developed to handle concept drift, but they are not suited for both sudden concept drift and gradual concept drift. Thus, there is a demand for an algorithm that can react to both the types of concept drift as well as incur less computational cost. A new approach hybrid early drift detection method (HEDDM) has been proposed for drift detection, which works with an ensemble method to improve the performance.

We are proposing a new data path design for a position emission tomography (PET) data acquisition system (DAQ). The proposed design can transmit, receive, and process all types of data through a single data stream. Instead of having separate path for data, clock and control signals individually, which is the general case of a conventional DAQ system, the proposed system uses a packet concept to identify and process different data types in a single fast data stream.

<abstract><p>With the widespread application of smart devices, the security of internet of things (IoT) systems faces entirely new challenges. The IoT data stream operates in a non-stationary, dynamic environment, making it prone to concept drift. This paper focused on addressing the issue of concept drift in data streams, with a key emphasis on introducing an innovative drift detection method-ensemble multiple non-parametric concept localization detectors, abbreviated as EMNCD. EMNCD employs an ensemble of non-parametric statistical methods, including the Kolmogorov-Smirnov, Wilcoxon rank sum and Mann-Kendall tests. By comparing sample distributions within a sliding window, EMNCD accurately detects concept drift, achieving precise localization of drift points, and enhancing overall detection reliability. Experimental results demonstrated the superior performance of EMNCD compared to classical methods on artificial datasets. Simultaneously, to enhance the robustness of data stream processing, we presented an online anomaly detection method based on the isolation forest (iForest). Additionally, we proposedwhale optimization algorithm (WOA)-extreme gradient boosting (XGBoost), a drift adaptation model employing XGBoost as a base classifier. This model dynamically updates using drift points detected by EMNCD and fine-tunes parameters through the WOA. Real-world applications on the edge-industrial IoTset (IIoTset) intrusion dataset explore the impact of concept drift on intrusion detection, where IIoT is a subclass of IoT. In summary, this paper focused on EMNCD, introducing innovative approaches for drift detection, anomaly detection, and drift adaptation. The research provided practical and viable solutions to address concept drift in data streams, enhancing security in IoT systems.</p></abstract>