scholarly journals An Improved Self-Training Method for Positive Unlabeled Time Series Classification Using DTW Barycenter Averaging

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7414
Author(s):  
Jing Li ◽  
Haowen Zhang ◽  
Yabo Dong ◽  
Tongbin Zuo ◽  
Duanqing Xu
Keyword(s):  
Time Series ◽  
Nearest Neighbor ◽  
Classification Problem ◽  
Training Data ◽  
Time Series Classification ◽  
Domain Experts ◽  
Average Sequence ◽  
The One ◽  
Public Datasets ◽  
Better Than

Traditional supervised time series classification (TSC) tasks assume that all training data are labeled. However, in practice, manually labelling all unlabeled data could be very time-consuming and often requires the participation of skilled domain experts. In this paper, we concern with the positive unlabeled time series classification problem (PUTSC), which refers to automatically labelling the large unlabeled set U based on a small positive labeled set PL. The self-training (ST) is the most widely used method for solving the PUTSC problem and has attracted increased attention due to its simplicity and effectiveness. The existing ST methods simply employ the one-nearest-neighbor (1NN) formula to determine which unlabeled time-series should be labeled. Nevertheless, we note that the 1NN formula might not be optimal for PUTSC tasks because it may be sensitive to the initial labeled data located near the boundary between the positive and negative classes. To overcome this issue, in this paper we propose an exploratory methodology called ST-average. Unlike conventional ST-based approaches, ST-average utilizes the average sequence calculated by DTW barycenter averaging technique to label the data. Compared with any individuals in PL set, the average sequence is more representative. Our proposal is insensitive to the initial labeled data and is more reliable than existing ST-based methods. Besides, we demonstrate that ST-average can naturally be implemented along with many existing techniques used in original ST. Experimental results on public datasets show that ST-average performs better than related popular methods.

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 IMPROVING TIME SERIES CLASSIFICATION ACCURACY: COMBINING GLOBAL AND LOCAL INFORMATION IN THE SIMILARITY CRITERION

2014 ◽  
Vol 44 (3) ◽  
pp. 225-229
Author(s):  
X. HE ◽  
C. SHAO ◽  
Y. XIONG
Keyword(s):  
Time Series ◽  
Nearest Neighbor ◽  
Similarity Criterion ◽  
Haar Wavelet ◽  
Local Information ◽  
Time Series Classification ◽  
Shape Information ◽  
Haar Wavelet Transform ◽  
Invariant Distance ◽  
Global And Local

Given the widespread use of time series classification in many domains, how to improve the accuracy of classification has attracted considerable focus. In this paper, a new similarity measure (SIMscl) based on the global and local information has been proposed for improving the precision rate of one nearest neighbor (1NN) classifier. Specifically, the global information records the intrinsic properties of time series, and is reflected by two indicators: the shape information and the complexity; the local information pays attention to the exact match of value, and is realized by LB_keogh. Simultaneously, a method based on multi-scale discrete haar wavelet transform, key point extraction, and symbolization has been put forward to extract the shape information. To test the efficacy of the proposed shape similarity SIMshape and hybrid similarity SIMscl, the experiments are conducted on two data sets: star light curve and beef. Experimental evaluations show that SIMshape can deal with some time series misclassified by Euclidean Distance (ED), LB_keogh, and Complexity Invariant Distance (CID), and SIMscl has higher precision than ED, LB_keogh, and CID in time series 1NN classification.

scholarly journals Comparing End-to-End Machine Learning Methods for Spectra Classification

2021 ◽  
Vol 11 (23) ◽  
pp. 11520
Author(s):  
Yue Sun ◽  
Sandor Brockhauser ◽  
Péter Hegedűs
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Short Term Memory ◽  
Classification Problem ◽  
High Energy ◽  
High Energy Density ◽  
Time Series Classification ◽  
Classification Models ◽  
Attention Model ◽  
End To End

In scientific research, spectroscopy and diffraction experimental techniques are widely used and produce huge amounts of spectral data. Learning patterns from spectra is critical during these experiments. This provides immediate feedback on the actual status of the experiment (e.g., time-resolved status of the sample), which helps guide the experiment. The two major spectral changes what we aim to capture are either the change in intensity distribution (e.g., drop or appearance) of peaks at certain locations, or the shift of those on the spectrum. This study aims to develop deep learning (DL) classification frameworks for one-dimensional (1D) spectral time series. In this work, we deal with the spectra classification problem from two different perspectives, one is a general two-dimensional (2D) space segmentation problem, and the other is a common 1D time series classification problem. We focused on the two proposed classification models under these two settings, the namely the end-to-end binned Fully Connected Neural Network (FCNN) with the automatically capturing weighting factors model and the convolutional SCT attention model. Under the setting of 1D time series classification, several other end-to-end structures based on FCNN, Convolutional Neural Network (CNN), ResNets, Long Short-Term Memory (LSTM), and Transformer were explored. Finally, we evaluated and compared the performance of these classification models based on the High Energy Density (HED) spectra dataset from multiple perspectives, and further performed the feature importance analysis to explore their interpretability. The results show that all the applied models can achieve 100% classification confidence, but the models applied under the 1D time series classification setting are superior. Among them, Transformer-based methods consume the least training time (0.449 s). Our proposed convolutional Spatial-Channel-Temporal (SCT) attention model uses 1.269 s, but its self-attention mechanism performed across spatial, channel, and temporal dimensions can suppress indistinguishable features better than others, and selectively focus on obvious features with high separability.

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.

Random Subclasses Ensembles by Using 1-Nearest Neighbor Framework

2017 ◽  
Vol 31 (10) ◽  
pp. 1750031
Author(s):  
Amir Ahmad ◽  
Hamza Abujabal ◽  
C. Aswani Kumar
Keyword(s):  
Nearest Neighbor ◽  
Naive Bayes ◽  
Ensemble Methods ◽  
Naïve Bayes ◽  
Training Data ◽  
Classifier Ensemble ◽  
Base Classifier ◽  
Decision Boundaries ◽  
Better Than

A classifier ensemble is a combination of diverse and accurate classifiers. Generally, a classifier ensemble performs better than any single classifier in the ensemble. Naive Bayes classifiers are simple but popular classifiers for many applications. As it is difficult to create diverse naive Bayes classifiers, naive Bayes ensembles are not very successful. In this paper, we propose Random Subclasses (RS) ensembles for Naive Bayes classifiers. In the proposed method, new subclasses for each class are created by using 1-Nearest Neighbor (1-NN) framework that uses randomly selected points from the training data. A classifier considers each subclass as a class of its own. As the method to create subclasses is random, diverse datasets are generated. Each classifier in an ensemble learns on one dataset from the pool of diverse datasets. Diverse training datasets ensure diverse classifiers in the ensemble. New subclasses create easy to learn decision boundaries that in turn create accurate naive Bayes classifiers. We developed two variants of RS, in the first variant RS(2), two subclasses per class were created whereas in the second variant RS(4), four subclasses per class were created. We studied the performance of these methods against other popular ensemble methods by using naive Bayes as the base classifier. RS(4) outperformed other popular ensemble methods. A detailed study was carried out to understand the behavior of RS ensembles.

On the stopping criteria for k -Nearest Neighbor in positive unlabeled time series classification problems

2016 ◽  
Vol 328 ◽  
pp. 42-59 ◽  
Author(s):  
Mabel González ◽  
Christoph Bergmeir ◽  
Isaac Triguero ◽  
Yanet Rodríguez ◽  
José M Benítez
Keyword(s):  
Time Series ◽  
Nearest Neighbor ◽  
Time Series Classification ◽  
Classification Problems ◽  
K Nearest Neighbor ◽  
Stopping Criteria

scholarly journals TEASER: early and accurate time series classification

2020 ◽  
Vol 34 (5) ◽  
pp. 1336-1362
Author(s):  
Patrick Schäfer ◽  
Ulf Leser
Keyword(s):  
Time Series ◽  
Real Life ◽  
Early Time ◽  
Classification Problem ◽  
Critical Issue ◽  
Decision Time ◽  
Superior Performance ◽  
Optimal Decision ◽  
Time Series Classification ◽  
Start Time

Abstract Early time series classification (eTSC) is the problem of classifying a time series after as few measurements as possible with the highest possible accuracy. The most critical issue of any eTSC method is to decide when enough data of a time series has been seen to take a decision: Waiting for more data points usually makes the classification problem easier but delays the time in which a classification is made; in contrast, earlier classification has to cope with less input data, often leading to inferior accuracy. The state-of-the-art eTSC methods compute a fixed optimal decision time assuming that every times series has the same defined start time (like turning on a machine). However, in many real-life applications measurements start at arbitrary times (like measuring heartbeats of a patient), implying that the best time for taking a decision varies widely between time series. We present TEASER, a novel algorithm that models eTSC as a two-tier classification problem: In the first tier, a classifier periodically assesses the incoming time series to compute class probabilities. However, these class probabilities are only used as output label if a second-tier classifier decides that the predicted label is reliable enough, which can happen after a different number of measurements. In an evaluation using 45 benchmark datasets, TEASER is two to three times earlier at predictions than its competitors while reaching the same or an even higher classification accuracy. We further show TEASER’s superior performance using real-life use cases, namely energy monitoring, and gait detection.

Sign in / Sign up

Export Citation Format

Share Document