scholarly journals Data Augmentation with Suboptimal Warping for Time-Series Classification

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 98 ◽  
Author(s):  
Krzysztof Kamycki ◽  
Tomasz Kapuscinski ◽  
Mariusz Oszust
Keyword(s):  
Time Series ◽  
Data Augmentation ◽  
Nearest Neighbor ◽  
Multivariate Time Series ◽  
Metric Learning ◽  
Classification Performance ◽  
Training Dataset ◽  
Time Series Classification ◽  
Extensive Evaluation ◽  
The Impact

In this paper, a novel data augmentation method for time-series classification is proposed. In the introduced method, a new time-series is obtained in warped space between suboptimally aligned input examples of different lengths. Specifically, the alignment is carried out constraining the warping path and reducing its flexibility. It is shown that the resultant synthetic time-series can form new class boundaries and enrich the training dataset. In this work, the comparative evaluation of the proposed augmentation method against related techniques on representative multivariate time-series datasets is presented. The performance of methods is examined using the nearest neighbor classifier with the dynamic time warping (NN-DTW), LogDet divergence-based metric learning with triplet constraints (LDMLT), and the recently introduced time-series cluster kernel (NN-TCK). The impact of the augmentation on the classification performance is investigated, taking into account entire datasets and cases with a small number of training examples. The extensive evaluation reveals that the introduced method outperforms related augmentation algorithms in terms of the obtained classification accuracy.

Time-Frequency Deep Metric Learning for Multivariate Time Series Classification

2021 ◽  
Author(s):  
Zhi Chen ◽  
Yongguo Liu ◽  
Jiajing Zhu ◽  
Yun Zhang ◽  
Rongjiang Jin ◽  
...  
Keyword(s):  
Time Series ◽  
Multivariate Time Series ◽  
Metric Learning ◽  
Time Series Classification ◽  
Time Frequency ◽  
Deep Metric Learning

scholarly journals WINkNN: Windowed Intervals’ Number kNN Classifier for Efficient Time-Series Applications

Mathematics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 413 ◽  
Author(s):  
Chris Lytridis ◽  
Anna Lekova ◽  
Christos Bazinas ◽  
Michail Manios ◽  
Vassilis G. Kaburlasos
Keyword(s):  
Time Series ◽  
Ad Hoc ◽  
Nearest Neighbor ◽  
Classification Performance ◽  
Human Robot Interaction ◽  
Time Series Classification ◽  
K Nearest Neighbor ◽  
Time Dimension ◽  
Knn Classifier ◽  
Benchmark Datasets

Our interest is in time series classification regarding cyber–physical systems (CPSs) with emphasis in human-robot interaction. We propose an extension of the k nearest neighbor (kNN) classifier to time-series classification using intervals’ numbers (INs). More specifically, we partition a time-series into windows of equal length and from each window data we induce a distribution which is represented by an IN. This preserves the time dimension in the representation. All-order data statistics, represented by an IN, are employed implicitly as features; moreover, parametric non-linearities are introduced in order to tune the geometrical relationship (i.e., the distance) between signals and consequently tune classification performance. In conclusion, we introduce the windowed IN kNN (WINkNN) classifier whose application is demonstrated comparatively in two benchmark datasets regarding, first, electroencephalography (EEG) signals and, second, audio signals. The results by WINkNN are superior in both problems; in addition, no ad-hoc data preprocessing is required. Potential future work is discussed.

scholarly journals A Metric Learning-Based Univariate Time Series Classification Method

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 288
Author(s):  
Kuiyong Song ◽  
Nianbin Wang ◽  
Hongbin Wang
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Metric Learning ◽  
Classification Method ◽  
Series Data ◽  
Classification Error ◽  
Time Series Classification ◽  
Classification Error Rate ◽  
Univariate Time Series

High-dimensional time series classification is a serious problem. A similarity measure based on distance is one of the methods for time series classification. This paper proposes a metric learning-based univariate time series classification method (ML-UTSC), which uses a Mahalanobis matrix on metric learning to calculate the local distance between multivariate time series and combines Dynamic Time Warping(DTW) and the nearest neighbor classification to achieve the final classification. In this method, the features of the univariate time series are presented as multivariate time series data with a mean value, variance, and slope. Next, a three-dimensional Mahalanobis matrix is obtained based on metric learning in the data. The time series is divided into segments of equal intervals to enable the Mahalanobis matrix to more accurately describe the features of the time series data. Compared with the most effective measurement method, the related experimental results show that our proposed algorithm has a lower classification error rate in most of the test datasets.

scholarly journals A Novel Channel and Temporal-Wise Attention in Convolutional Networks for Multivariate Time Series Classification

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 212247-212257
Author(s):  
Xu Cheng ◽  
Peihua Han ◽  
Guoyuan Li ◽  
Shengyong Chen ◽  
Houxiang Zhang
Keyword(s):  
Time Series ◽  
Multivariate Time Series ◽  
Time Series Classification ◽  
Convolutional Networks

scholarly journals Automated classification of clinical trial eligibility criteria text based on ensemble learning and metric learning

2021 ◽  
Vol 21 (S2) ◽  
Author(s):  
Kun Zeng ◽  
Yibin Xu ◽  
Ge Lin ◽  
Likeng Liang ◽  
Tianyong Hao
Keyword(s):  
Clinical Trial ◽  
Ensemble Learning ◽  
Metric Learning ◽  
Classification Performance ◽  
Ensemble Model ◽  
Automated Classification ◽  
Eligibility Criteria ◽  
Data Imbalance ◽  
The Impact

Abstract Background Eligibility criteria are the primary strategy for screening the target participants of a clinical trial. Automated classification of clinical trial eligibility criteria text by using machine learning methods improves recruitment efficiency to reduce the cost of clinical research. However, existing methods suffer from poor classification performance due to the complexity and imbalance of eligibility criteria text data. Methods An ensemble learning-based model with metric learning is proposed for eligibility criteria classification. The model integrates a set of pre-trained models including Bidirectional Encoder Representations from Transformers (BERT), A Robustly Optimized BERT Pretraining Approach (RoBERTa), XLNet, Pre-training Text Encoders as Discriminators Rather Than Generators (ELECTRA), and Enhanced Representation through Knowledge Integration (ERNIE). Focal Loss is used as a loss function to address the data imbalance problem. Metric learning is employed to train the embedding of each base model for feature distinguish. Soft Voting is applied to achieve final classification of the ensemble model. The dataset is from the standard evaluation task 3 of 5th China Health Information Processing Conference containing 38,341 eligibility criteria text in 44 categories. Results Our ensemble method had an accuracy of 0.8497, a precision of 0.8229, and a recall of 0.8216 on the dataset. The macro F1-score was 0.8169, outperforming state-of-the-art baseline methods by 0.84% improvement on average. In addition, the performance improvement had a p-value of 2.152e-07 with a standard t-test, indicating that our model achieved a significant improvement. Conclusions A model for classifying eligibility criteria text of clinical trials based on multi-model ensemble learning and metric learning was proposed. The experiments demonstrated that the classification performance was improved by our ensemble model significantly. In addition, metric learning was able to improve word embedding representation and the focal loss reduced the impact of data imbalance to model performance.

Sign in / Sign up

Export Citation Format

Share Document