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Found 24 papers, 17 papers with code
Improving the Evaluation and Actionability of Explanation Methods for Multivariate Time Series Classification
Unlike related work, we go beyond evaluation and also showcase the actionability of the produced explainer ranking, by using the best attribution methods for the task of channel selection in MTSC.
Machine Vision-Enabled Sports Performance Analysis
$\textbf{Goal:}$ This study investigates the feasibility of monocular 2D markerless motion capture (MMC) using a single smartphone to measure jump height, velocity, flight time, contact time, and range of motion (ROM) during motor tasks.
Human Activity Segmentation Challenge @ ECML/PKDD’23
Despite its importance, existing methods demonstrate limited efficacy on real-world multivariate time series data.
Evaluating Explanation Methods for Multivariate Time Series Classification
In many applications classification alone is not enough, we often need to classify but also understand what the model learns (e. g., why was a prediction given, based on what information in the data).
Back to Basics: A Sanity Check on Modern Time Series Classification Algorithms
The state-of-the-art in time series classification has come a long way, from the 1NN-DTW algorithm to the ROCKET family of classifiers.
An Examination of Wearable Sensors and Video Data Capture for Human Exercise Classification
Additionally, a minimum of 3 IMUs are required to outperform a single camera.
Robust Explainer Recommendation for Time Series Classification
Recently, a great variety of techniques have been proposed and adapted for time series to provide explanation in the form of saliency maps, where the importance of each data point in the time series is quantified with a numerical value.
Quantifying Jump Height Using Markerless Motion Capture with a Single Smartphone
This study evaluates how accurately markerless motion capture (MMC) with a single smartphone can measure bilateral and unilateral CMJ jump height.
Classification of cow diet based on milk mid infrared spectra: a data analysis competition at the "International workshop of spectroscopy and chemometrics 2022"
In April 2022, the Vistamilk SFI Research Centre organized the second edition of the "International Workshop on Spectroscopy and Chemometrics - Applications in Food and Agriculture".
Fast and Robust Video-Based Exercise Classification via Body Pose Tracking and Scalable Multivariate Time Series Classifiers
We analyze the accuracy and robustness of BodyMTS and show that it is robust to different types of noise caused by either video quality or pose estimation factors.
Scalable Classifier-Agnostic Channel Selection for Multivariate Time Series Classification
Channel selection is applied as a pre-processing step before training state-of-the-art MTSC algorithms and saves about 70\% of computation time and data storage, with preserved accuracy.
Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement Learning
Sentence compression reduces the length of text by removing non-essential content while preserving important facts and grammaticality.
Automated Mobility Context Detection with Inertial Signals
We explore two different approaches to this task: (1) using gait descriptors and features extracted from the input inertial signals sampled during walking episodes, together with classic machine learning algorithms, and (2) treating the input inertial signals as time series data and leveraging end-to-end state-of-the-art time series classifiers.
MrSQM: Fast Time Series Classification with Symbolic Representations
The key idea is to transform numerical time series to symbolic representations in the time or frequency domain, i. e., sequences of symbols, and then extract features from these sequences.
Mid infrared spectroscopy and milk quality traits: a data analysis competition at the "International Workshop on Spectroscopy and Chemometrics 2021"
A chemometric data analysis challenge has been arranged during the first edition of the "International Workshop on Spectroscopy and Chemometrics", organized by the Vistamilk SFI Research Centre and held online in April 2021.
Interpretability of a Deep Learning Model in the Application of Cardiac MRI Segmentation with an ACDC Challenge Dataset
In previous studies, the base method is applied to the classification of cardiac disease and provides clinically meaningful explanations for the predictions of a black-box deep learning classifier.
Background Knowledge Injection for Interpretable Sequence Classification
Sequence classification is the supervised learning task of building models that predict class labels of unseen sequences of symbols.
Interpretable Time Series Classification using Linear Models and Multi-resolution Multi-domain Symbolic Representations
In this paper we propose new time series classification algorithms to address these gaps.
A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal
Multi-document summarization (MDS) aims to compress the content in large document collections into short summaries and has important applications in story clustering for newsfeeds, presentation of search results, and timeline generation.
Examining the State-of-the-Art in News Timeline Summarization
Previous work on automatic news timeline summarization (TLS) leaves an unclear picture about how this task can generally be approached and how well it is currently solved.
Story Disambiguation: Tracking Evolving News Stories across News and Social Streams
This is particularly the case for local news stories that are easily over shadowed by other trending stories, and for complex news stories with ambiguous content in noisy stream environments.
Interpretable Time Series Classification using All-Subsequence Learning and Symbolic Representations in Time and Frequency Domains
In this work we analyse the state-of-the-art for time series classification, and propose new algorithms that aim to maintain the classifier accuracy and efficiency, but keep interpretability as a key design constraint.
A Machine Learning Approach to Predicting the Smoothed Complexity of Sorting Algorithms
Smoothed analysis is a framework for analyzing the complexity of an algorithm, acting as a bridge between average and worst-case behaviour.
