Extreme Verification Latency Research Articles

Adaptation for Automated Drift Detection in Electromechanical Machine Monitoring.

Practical machine learning applications for streaming data can involve concept drift (the change in statistical properties of data over time), one-shot or few-shot learning (starting with only one or a few examples for each class), a scarcity of representative training data, and extreme verification latency (only the initial dataset has ground-truth labels). This work presents a framework for organizing signal processing and machine learning techniques to provide adaptive classification and drift detection. Nonintrusive load monitoring (NILM) serves as an ideal case study, as modern sensing solutions provide a wellspring of electromechanical data sources. There is a lack of training datasets that generalize across different load and fault scenarios. Accordingly, training must be accomplished with a limited set of data when deploying a NILM to a new power system. Also, loads can exhibit concept drift over time either due to faults or normal variation. NILM field data is used as an illustrative case study to demonstrate the proposed framework for adaptation and drift tracking.

Novelty detection for multi-label stream classification under extreme verification latency

Multi-Label Stream Classification (MLSC) is the classification streaming examples into multiple classes simultaneously. Since new classes may emerge during the streaming process (concept evolution) and known classes may change over time (concept drift) it is challenging task. In real situations, concept drift and concept evolution occur in scenarios where the actual labels of arriving examples are never available; hence it is impractical to update decision models in a supervised fashion. This is known as Extreme Verification Latency, a topic that has not been well investigated in MLSC literature. This paper proposes a new method called MultI-label learNing Algorithm for Data Streams with Binary Relevance transformation (MINAS-BR), integrated with a Novelty Detection (ND) procedure for detecting concept evolution and concept drift, updating the model in an unsupervised fashion. Furthermore, since the label space is not static, we propose a new evaluation methodology for MLSC under extreme verification latency. Experiments over synthetic and real-world data sets with different concept drift and concept evolution scenarios confirmed the strategies employed in the MINAS-BR and presented relevant advances for handling streaming multi-label data.

Adaptive Learning With Extreme Verification Latency in Non-Stationary Environments

Existing Data Stream Mining algorithms assume the availability of labelled and balanced data streams. However, in many real-world applications such as Robotics, Weather Monitoring, Fraud-Detection systems, Cyber Security, and Human Activity Recognition, a vast amount of high-speed data is generated by Internet of Things sensors and real-time data on the Internet are unlabelled. Furthermore, the prediction models need to learn in Non-Stationary Environments due to evolving concepts. Manual labelling of these data streams is not practical due to the need for domain expertise and the time-resource-prohibitive nature of the required effort. To deal with such scenarios, existing approaches are self-Learning or Cluster-Guided Classification (CGC) which predict the pseudo-labels, which further update the prediction models. Previous studies have yet to establish a clear and conclusive view as to when, and why one pseudo-labelling approach should be preferable to another and what causes an approach to fail. In this research, we propose a novel approach, “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Predictor for Streaming Data with Scarce Labels</i> ” (PSDSL), which is capable of intelligently switching between self-learning, CGC and micro-clustering strategies, based on the problem it is applied to, i.e., the different characteristics of the data streams. In PSDSL a novel approach called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Envelope-Clustering</i> has been introduced to resolve the conflict during the cluster labelling which suggested a confidence measure approach to ensure the quality and correctness of labels assigned to the clusters. The auto parameter tuning mechanism of PSDSL eliminates the human dependency and determines the best value of number of centroids from initial labelled data. The predictive performance of the PSDSL is evaluated on non-stationary datasets, synthetic data-streams, and real-world datasets. The approach has shown promising results on randomised datasets as well as on synthetic data-streams, as compared with state-of-the-art approaches. This is the first large-scale study on an adaptive extreme verification approach that supports automatic parameter tuning and intelligent switching of pseudo-labelling strategy, thus reducing the dependency of machine learning on human input.

AMANDA: Semi-supervised density-based adaptive model for non-stationary data with extreme verification latency

Concept drift refers to an alteration in the relations between input and output data in the distribution over time. Thus, a gradual concept drift alludes to a smooth and gradual change in these relations. It generates a model obsolescence and quality decrease in predictions. Besides, there is a challenging task: the extreme verification latency to certify the labels. For batch scenarios, state-of-the-art methods do not properly tackle the problems aforementioned due to their high computational time, lack of representing samples of the drift or even for having several hyperparameters for tuning. Therefore, we propose AMANDA, a semi-supervised density-based adaptive model for non-stationary data. It has two variations: AMANDA-FCP, which selects a fixed number of samples; and AMANDA-DCP, which, in turn, dynamically selects samples from data. Our results indicate that these two variations outperform the state-of-the-art methods for almost all synthetic and real datasets, with an improvement up to 27.98% regarding the average error. AMANDA-FCP improved the results for a gradual concept drift, even with a small size of initial labeled data. Moreover, our results indicate that semi-supervised classifiers are improved when they work along with our density-based methods. Therefore, we emphasize the importance of research directions based on this approach.

A Novelty Detector and Extreme Verification Latency Model for Nonstationary Environments

Safe and reliable operation of systems relies on the use of online condition monitoring and diagnostic systems that aim to take immediate actions upon the occurrence of a fault. Model-based solutions are often not practical in nonstationary environments. Thus, the evolving data stream requires the data-driven model to be adaptive. In this paper, we propose a framework for the fault detection and classification that is accomplished on the data stream with both the gradual and abrupt drifts. The framework is only provided with prior information about the possible faults at the initial step; however, despite this, the framework can still detect the novel faults without receiving any update. Furthermore, an efficient fault classification algorithm is presented to maximize the efficiency of the proposed framework. Finally, the proposed framework is applied for diagnosing bearing defects in the induction motors to demonstrate its feasibility for industrial applications.

COMPOSE: A semisupervised learning framework for initially labeled nonstationary streaming data.

An increasing number of real-world applications are associated with streaming data drawn from drifting and nonstationary distributions that change over time. These applications demand new algorithms that can learn and adapt to such changes, also known as concept drift. Proper characterization of such data with existing approaches typically requires substantial amount of labeled instances, which may be difficult, expensive, or even impractical to obtain. In this paper, we introduce compacted object sample extraction (COMPOSE), a computational geometry-based framework to learn from nonstationary streaming data, where labels are unavailable (or presented very sporadically) after initialization. We introduce the algorithm in detail, and discuss its results and performances on several synthetic and real-world data sets, which demonstrate the ability of the algorithm to learn under several different scenarios of initially labeled streaming environments. On carefully designed synthetic data sets, we compare the performance of COMPOSE against the optimal Bayes classifier, as well as the arbitrary subpopulation tracker algorithm, which addresses a similar environment referred to as extreme verification latency. Furthermore, using the real-world National Oceanic and Atmospheric Administration weather data set, we demonstrate that COMPOSE is competitive even with a well-established and fully supervised nonstationary learning algorithm that receives labeled data in every batch.