A SMOTE Extension for Balancing Multivariate Epilepsy-Related Time Series Datasets

Cal, Enrique A. de la; Villar, José R.; Vergara, Paula M.; Sedano, Javier; Herrero, Álvaro

doi:10.1007/978-3-319-67180-2_43

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…SSB also occurs in time series data, affecting time series classification, forecasting, and anomaly detection [49]. Resampling approaches (including SMOTE) have been adapted to the time series domain in, e.g., [50][51][52]. More generally, resampling is also one approach for data augmentation, a collection of techniques for perturbing examples from a dataset to create new synthetic examples that help to improve deep neural network training [53].…”

Section: Sample Selection Bias Mitigation In Time Seriesmentioning

confidence: 99%

Deep-Shallow Metaclassifier with Synthetic Minority Oversampling for Anomaly Detection in a Time Series

Reshadi,

Li,

et al. 2024

Algorithms

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Anomaly detection in data streams (and particularly time series) is today a vitally important task. Machine learning algorithms are a common design for achieving this goal. In particular, deep learning has, in the last decade, proven to be substantially more accurate than shallow learning in a wide variety of machine learning problems, and deep anomaly detection is very effective for point anomalies. However, deep semi-supervised contextual anomaly detection (in which anomalies within a time series are rare and none at all occur in the algorithm’s training data) is a more difficult problem. Hybrid anomaly detectors (a “normal model” followed by a comparator) are one approach to these problems, but the separate loss functions for the two components can lead to inferior performance. We investigate a novel synthetic-example oversampling technique to harmonize the two components of a hybrid system, thus improving the anomaly detector’s performance. We evaluate our algorithm on two distinct problems: identifying pipeline leaks and patient-ventilator asynchrony.

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Section: Sample Selection Bias Mitigation In Time Seriesmentioning

confidence: 99%

Deep-Shallow Metaclassifier with Synthetic Minority Oversampling for Anomaly Detection in a Time Series

Reshadi,

Li,

et al. 2024

Algorithms

View full text Add to dashboard Cite

show abstract

“…Alternatively, artificial fall data can be collected in controlled laboratory settings, but they may not be the best representatives of actual falls [42]. Moreover, classification models built with artificial falls are more likely to suffer from the problem of over-fitting, caused by time series dataset imbalance [43,44], and may poorly generalize actual falls. In this case, we propose an MGD-based classifier, which does not require fall data in training phase, for detecting LIA, MIA, VIA, and fall.…”

Section: User-adaptive Algorithm For Activity Recognitionmentioning

confidence: 99%

A User-Adaptive Algorithm for Activity Recognition Based on K-Means Clustering, Local Outlier Factor, and Multivariate Gaussian Distribution

Zhao

Cao

2018

Sensors

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Mobile activity recognition is significant to the development of human-centric pervasive applications including elderly care, personalized recommendations, etc. Nevertheless, the distribution of inertial sensor data can be influenced to a great extent by varying users. This means that the performance of an activity recognition classifier trained by one user’s dataset will degenerate when transferred to others. In this study, we focus on building a personalized classifier to detect four categories of human activities: light intensity activity, moderate intensity activity, vigorous intensity activity, and fall. In order to solve the problem caused by different distributions of inertial sensor signals, a user-adaptive algorithm based on K-Means clustering, local outlier factor (LOF), and multivariate Gaussian distribution (MGD) is proposed. To automatically cluster and annotate a specific user’s activity data, an improved K-Means algorithm with a novel initialization method is designed. By quantifying the samples’ informative degree in a labeled individual dataset, the most profitable samples can be selected for activity recognition model adaption. Through experiments, we conclude that our proposed models can adapt to new users with good recognition performance.

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DTW as Alignment Function in the Context of Time Series Balancing

Cal

Villar

Sedano

2019

Advances in Intelligent Systems and Computing

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A SMOTE Extension for Balancing Multivariate Epilepsy-Related Time Series Datasets

Cited by 5 publications

References 14 publications

Deep-Shallow Metaclassifier with Synthetic Minority Oversampling for Anomaly Detection in a Time Series

Deep-Shallow Metaclassifier with Synthetic Minority Oversampling for Anomaly Detection in a Time Series

A User-Adaptive Algorithm for Activity Recognition Based on K-Means Clustering, Local Outlier Factor, and Multivariate Gaussian Distribution

DTW as Alignment Function in the Context of Time Series Balancing

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