Semi-Supervised Change-Point Detection via Consistency Training on Augmented Temporal Views

A synthetic informative minority over-sampling (SIMO) algorithm leveraging support vector machine to enhance learning from imbalanced datasets

In this study, the performance of moving cut data-approximate entropy (MC-ApEn) to detect abrupt dynamic changes was investigated. Numerical tests in a time series model indicate that the MC-ApEn method is suitable for the detection of abrupt dynamic changes for three types of meteorological data: daily maximum temperature, daily minimum temperature, and daily precipitation. Additionally, the MC-ApEn method was used to detect abrupt climate changes in daily precipitation data from Northwest China and the Pacific Decadal Oscillation (PDO) index. The results show an abrupt dynamic change in precipitation in 1980 and in the PDO index in 1976. The times indicated for the abrupt changes are identical to those from previous results. Application of the analysis to observational data further confirmed the performance of the MC-ApEn method. Moreover, MC-ApEn outperformed the moving t test (MTT) and the moving detrended fluctuation analysis (MDFA) methods for the detection of abrupt dynamic changes in a simulated 1000-point daily precipitation dataset.

Feature selection poses a common challenge in data mining and machine learning. This paper offers a comprehensive overview of the field, encompassing its historical context, recent advancements, and associated challenges. Topics covered include categorization of feature selection methods, development of ensemble frameworks, and assessment of selection stability. Current research trends in feature selection and computational intelligence are examined, alongside practical applications in ensemble configuration, threshold detection, evaluation criteria, and stability enhancement techniques. This article is categorized under: Algorithmic Development > Ensemble Methods

An accurate prediction of complex product quality parameters from process time series by an end-to-end learning approach remains a significant challenge in machine learning. A special difficulty is the application of industrial batch process data because many batch processes generate variable length time series. In the industrial application of such methods, explainability is often desired. In this study, a 1D convolutional neural network (CNN) algorithm with a masking layer is proposed to solve the problem for time series of variable length. In addition, a novel combination of 1D CNN and class activation mapping (CAM) technique is part of this study to better understand the model results and highlight some regions of interest in the time series. As a comparative state-of-the-art unsupervised machine learning method, the One-Nearest Neighbours (1NN) algorithm combined with dynamic time warping (DTW) was used. Both methods are investigated as end-to-end learning methods with balanced and unbalanced class distributions and with scaled and unscaled input data, respectively. The FastDTW and DTAIDistance algorithms were investigated for the DTW calculation. The data set is made up of sensor signals that was collected during the production of plastic parts. The objective was to predict a quality parameter of plastic parts during production. For this research, the quality parameter will be a difficult or only destructively measurable parameter and both methods will be investigated for their applicability to this prediction task. The application of the proposed approach to an industrial facility for producing plastic products shows a prediction accuracy of 83.7%. It can improve the reverence method by approximately 1.4%. In addition to the slight increase in accuracy, the CNN training time was significantly reduced compared to the DTW calculation.

Sequential Detection of Abrupt Changes in ARMA Models

This study is devoted to detection of abrupt changes in electrocardiogram (ECG). A linear time-variant model with Gaussian white noise is used to describe the real ECG signal, based on the estimated system parameters and tuned covariances of noise, the off-line and on-line generalised likelihood ratio (GLR) tests for ECG signal are developed for change detection. For comparison, the test algorithm uses Levinson, recursive least squares (RLS) methods to obtain the filter models parameters of ECG. Furthermore, windowed on-line GLR test algorithm is developed, which works more effectively in real-time situation. The simulation results with real data show the effectiveness of the application.

Long-term trend and abrupt changes of major climate variables in the Taihu Basin were investigated based on the mean, maximum and minimum air temperature, diurnal temperature range (DTR), precipitation, relative humidity and sunshine duration at six meteorological stations from 1954 to 2006. Results showed that the long-term trend for annual precipitation was not statistically significant during the past 53 years, but a wetter tendency was detected and the increasing centre for annual precipitation was located in the southeast of the study area. Mean air temperature has increased by 1.43°C, similar trends were also identified for maximum and minimum air temperature, which have increased by 1.06 and 1.54°C, respectively, while DTR exhibited a slight decreasing trend with a rate of about −0.09°C/(10 year). The annual mean relative humidity and sunshine duration exhibited a decreasing trend, with Kendall slope values of −0.99%/10 year and −7.797 h/10 year, respectively. Examination of long-range dependence showed that all climate variables exhibited strong persistence at annual scale except minimum air temperature. Detection of abrupt changes using nonparametric Mann–Kendall and Pettitt methods showed different results. Abrupt changes occurred in the 1980s and 2000s for annual precipitation using the nonparametric Mann–Kendall method, while no abrupt changes were detected using the Pettitt method. Abrupt changes of temperature and relative humidity took place in the early 1990s using the nonparametric Mann–Kendall method, which occurred in the late and mid 1980s using the Pettitt method, while abrupt changes of sunshine duration and DTR detected by two methods occurred in the similar period. The result will be helpful for local flood control and drought relief in urban planning and construction under future global climate change.

This paper addresses the problem of accurate estimation and rapid adaptation of abrupt frequency changes from noisy sinusoidal signals. A two‐filter adaptive algorithm is proposed to achieve the seemingly contradictory objectives. This algorithm is derived from an iterative batch procedure that has been proved to yield consistent and asymptotically Gaussian estimates for constant‐frequency estimation. These properties provide a basis for the detection of abrupt changes in the time‐varying frequency. Combining the high accuracy of narrow‐band/long‐memory filtering with the high adaptability of wide‐band/short‐memory filtering, the two‐filter approach is particularly suitable for tracking time‐varying frequencies that can be approximately modeled as piecewise‐constant functions of time. Simulation results are reported that justify the viability of the method.

The historical flood-level time series of the River Nile (AD 622–1470) is chosen to identify abrupt climate changes by applying global and local analysis techniques: the Mann–Kendall test and a non-hierarchical cluster analysis method to improve the Mann–Kendall test; a multiscale moving t-test with correction to the degree of freedom and an antisymmetric wavelet transform. The global estimates show three distinct epochs, AD 622–1078, 1079–1325 and 1326–1470, coinciding with larger scale climate changes: a relatively cool age, the Little Climatic Optimum of the Middle Ages, and an interim period before the Little Ice Age. The local estimates reveal the following results. The reference time of abrupt changes can be clearly identified, the associated time-scale coincides with the persistent anomaly period, and the maximum absolute t-value is statistically significant. There are about eight almost synchronous abrupt changes in the minimum and maximum River Nile flood levels, many of them are associated with 35–45 year persistence time-scales. An association of these short time-scales with those of interdecadal variability reported for the mid- and high-latitude sea-surface temperature of the North Atlantic is suggested, although information on phase coherence is not available. ©1997 by the Royal Meteorological Society. Int. J Climatol., 17: 1301–1315 (1997) (No. of Figures: 9 No. of Tables: 1 No. of

In recent decades, the streamflow and sediment of the Yellow River has decreased sharply, especially the sediment discharge. The factors that lead to this phenomenon have become a widely concerned problem of the whole society. The analysis of abrupt change points of hydrological series is the key to divide datum period, so it is an important work in the research of streamflow and sediment reduction cause. So far, many methods have been proposed to detect abrupt change. However, most methods have great uncertainty due to the deficiencies of irrational structure of test statistics, ideal hypothesis or subjectivity. In this paper, a new method called moving average difference method for abrupt change points detection is proposed. It is proved to be effective through comparison with four commonly used methods via both synthetic series and real data case study. The results show that the proposed method has four distinct advantages: (1) The test statistic structure of the method has physical significance and is intuitive to understand; (2) It is more accurate in abrupt change detection; (3) It can detect all of the abrupt change points at one time; (4) It can detect the abrupt changes and calculate the corresponding mutation intensity simultaneously.

Stroke is a leading cause of disability and mortality worldwide, necessitating the development of advanced technologies to improve its diagnosis, treatment, and patient outcomes. In recent years, machine learning techniques have emerged as promising tools in stroke medicine, enabling efficient analysis of large-scale datasets and facilitating personalized and precision medicine approaches. This abstract provides a comprehensive overview of machine learning's applications, challenges, and future directions in stroke medicine. Recently introduced machine learning algorithms have been extensively employed in all the fields of stroke medicine. Machine learning models have demonstrated remarkable accuracy in imaging analysis, diagnosing stroke subtypes, risk stratifications, guiding medical treatment, and predicting patient prognosis. Despite the tremendous potential of machine learning in stroke medicine, several challenges must be addressed. These include the need for standardized and interoperable data collection, robust model validation and generalization, and the ethical considerations surrounding privacy and bias. In addition, integrating machine learning models into clinical workflows and establishing regulatory frameworks are critical for ensuring their widespread adoption and impact in routine stroke care. Machine learning promises to revolutionize stroke medicine by enabling precise diagnosis, tailored treatment selection, and improved prognostication. Continued research and collaboration among clinicians, researchers, and technologists are essential for overcoming challenges and realizing the full potential of machine learning in stroke care, ultimately leading to enhanced patient outcomes and quality of life. This review aims to summarize all the current implications of machine learning in stroke diagnosis, treatment, and prognostic evaluation. At the same time, another purpose of this paper is to explore all the future perspectives these techniques can provide in combating this disabling disease.

Until now, much of the work on machine learning and health has focused on processes inside the hospital or clinic. However, this represents only a narrow set of tasks and challenges related to health; there is greater potential for impact by leveraging machine learning in health tasks more broadly. In this Perspective we aim to highlight potential opportunities and challenges for machine learning within a holistic view of health and its influences. To do so, we build on research in population and public health that focuses on the mechanisms between different cultural, social and environmental factors and their effect on the health of individuals and communities. We present a brief introduction to research in these fields, data sources and types of tasks, and use these to identify settings where machine learning is relevant and can contribute to new knowledge. Given the key foci of health equity and disparities within public and population health, we juxtapose these topics with the machine learning subfield of algorithmic fairness to highlight specific opportunities where machine learning, public and population health may synergize to achieve health equity. Algorithmic solutions to improve treatment are starting to transform health care. Mhasawade and colleagues discuss in this Perspective how machine learning applications in population and public health can extend beyond clinical practice. While working with general health data comes with its own challenges, most notably ensuring algorithmic fairness in the face of existing health disparities, the area provides new kinds of data and questions for the machine learning community.

Semi-supervised learning (SSL) aims to train a machine learning (ML) model using both labeled and unlabeled data. While the unlabeled data have been used in various ways to improve the prediction accuracy, the reason why unlabeled data could help is not fully understood. One interesting and promising direction is to understand SSL from a causal perspective. In light of the independent causal mechanisms (ICM) principle, the unlabeled data can be helpful when the label causes the features but not vice versa. However, the causal relations between the features and labels can be complex in real world applications. In this article, we propose an SSL framework that works with general causal models in which the variables have flexible causal relations. More specifically, we explore the causal graph structures and design corresponding causal generative models which can be learned with the help of unlabeled data. The learned causal generative model can generate synthetic labeled data for training a more accurate predictive model. We verify the effectiveness of our proposed method by empirical studies on both simulated and real data.

In this paper we explore some of the opportunities and challenges for machine learning on the Semantic Web. The Semantic Web provides standardized formats for the representation of both data and ontological background knowledge. Semantic Web standards are used to describe meta data but also have great potential as a general data format for data communication and data integration. Within a broad range of possible applications machine learning will play an increasingly important role: Machine learning solutions have been developed to support the management of ontologies, for the semi-automatic annotation of unstructured data, and to integrate semantic information into web mining. Machine learning will increasingly be employed to analyze distributed data sources described in Semantic Web formats and to support approximate Semantic Web reasoning and querying. In this paper we discuss existing and future applications of machine learning on the Semantic Web with a strong focus on learning algorithms that are suitable for the relational character of the Semantic Web's data structure. We discuss some of the particular aspects of learning that we expect will be of relevance for the Semantic Web such as scalability, missing and contradicting data, and the potential to integrate ontological background knowledge. In addition we review some of the work on the learning of ontologies and on the population of ontologies, mostly in the context of textual data.

During the past decade, it has been quite successful to observe how computational topology has incorporated some of the fundamental concepts and ideas from algebraic and differential topology into applications. This fusion of theories gave birth to a new field named Topological Data Analysis (TDA), which has a significant value in various fields, ranging from computational biology to personalized medicine and dynamic data analysis. Going beyond its foundational applications, TDA has enriched and complemented classical machine and deep learning frameworks in establishing what is now known as “topological machine learning.” In this paper, we review the present landscape of this emerging field, emphasizing how it merges with machine learning algorithms such as deep neural networks. Each method confers special advantages, targeting areas like machine learning integration, network reconstruction, classification of network regimes, or reduction of noisy data. We described common methodologies, discussed current implementations, and anticipate future challenges in topological machine learning.