Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Aggregates Classification01:29

Aggregates Classification

362
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
362
Classification of Signals01:30

Classification of Signals

685
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
685
Force Classification01:22

Force Classification

1.4K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.4K
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

114
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
114
Classification of Systems-I01:26

Classification of Systems-I

262
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
262
Classification of Systems-II01:31

Classification of Systems-II

212
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
212

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Securing Cyber-Physical Water Infrastructures: A Hybrid Intrusion Detection System for IoT Telemetry and Industrial Protocols.

Sensors (Basel, Switzerland)·2026
Same author

Data-Driven Automated Cardiac Health Management with Robust Edge Analytics and De-Risking.

Sensors (Basel, Switzerland)·2019
Same author

White Box Implementations Using Non-Commutative Cryptography.

Sensors (Basel, Switzerland)·2019
Same author

HyRA: A Hybrid Recommendation Algorithm Focused on Smart POI. Ceutí as a Study Scenario.

Sensors (Basel, Switzerland)·2018
Same author

Optimized ECC Implementation for Secure Communication between Heterogeneous IoT Devices.

Sensors (Basel, Switzerland)·2015
Same author

IPv6 addressing proxy: mapping native addressing from legacy technologies and devices to the Internet of Things (IPv6).

Sensors (Basel, Switzerland)·2013
Same journal

Analysis of strength degradation of coal and rock masses and stability of mined areas under long term immersion environment.

PloS one·2026
Same journal

Biogenic Silver-Selenium nanocomposite with anticancer activity and potent efficacy against vancomycin-resistant Staphylococcus aureus.

PloS one·2026
Same journal

Preparation and physicochemical characterization of a biodegradable chitosan/carboxymethyl cellulose hydrogel synthesized in NaOH/urea medium.

PloS one·2026
Same journal

Action-guilt, survivor-guilt, and depression in combat-related PTSD.

PloS one·2026
Same journal

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PloS one·2026
Same journal

Deep learning based two-way feature depiction model for brain tumor detection.

PloS one·2026
See all related articles

Related Experiment Video

Updated: Aug 20, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

294

When less is more powerful: Shapley value attributed ablation with augmented learning for practical time series

Arijit Ukil1, Leandro Marin2, Antonio J Jara3

  • 1TCS Research, Tata Consultancy Services, Kolkata, India.

Plos One
|November 23, 2022
PubMed
Summary
This summary is machine-generated.

Addressing limited training data in time series classification, Shapley Attributed Ablation with Augmented Learning (ShapAAL) enhances deep learning models. This method selects key data subsets and augments training for improved performance in tasks like cardiovascular disease detection from ECG data.

More Related Videos

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.0K
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.4K

Related Experiment Videos

Last Updated: Aug 20, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

294
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.0K
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.4K

Area of Science:

  • Machine Learning and Data Science
  • Biomedical Signal Processing

Background:

  • Time series sensor data classification, crucial for applications like cardiovascular disease (CVD) detection from Electrocardiogram (ECG) data, is often hindered by the scarcity of expertly annotated training data.
  • State-of-the-art deep learning models typically require large datasets, posing a challenge for domains with expensive or time-consuming data labeling processes.

Purpose of the Study:

  • To propose a novel deep learning framework, Shapley Attributed Ablation with Augmented Learning (ShapAAL), designed to improve classification performance on time series sensor data despite limited training examples.
  • To demonstrate that strategic data subset selection and augmented training can overcome data scarcity issues in supervised learning.

Main Methods:

  • ShapAAL employs an augmented training strategy using a Residual Network (ResNet) architecture, generating perturbation-induced inputs to expand the input space and compensate for data scarcity.
  • Shapley value attribution is utilized to identify and select a subset of the most informative training examples from the augmented dataset, enhancing model learnability.
  • This approach functions as a push-pull deep architecture, where Shapley value attribution refines the model by focusing on essential data, while augmented training broadens its learning capacity.

Main Results:

  • Empirical ablation studies confirm the effectiveness of the ShapAAL method.
  • ShapAAL consistently outperforms existing baseline and state-of-the-art algorithms on various time series classification tasks.
  • The method shows particular promise in critical applications such as the detection of CVDs from ECG data, using datasets from the UCR time series archive.

Conclusions:

  • ShapAAL offers a robust solution for time series classification tasks plagued by limited training data, achieving superior predictive performance.
  • The integration of Shapley value-based subset selection and augmented training presents a significant advancement in leveraging scarce data for deep learning models.
  • The proposed method has broad applicability in scientific and medical domains requiring accurate classification from limited sensor data.