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 Experiment Videos

Integrated sensing and processing decision trees.

Carey E Priebe1, David J Marchette, Dennis M Healy

  • 1Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA. cep@jhu.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|June 27, 2008
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Graph Neural Networks Powered by Encoder Embedding for Improved Node Learning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Multiscale comparative connectomics.

Imaging neuroscience (Cambridge, Mass.)·2025
Same author

Simple Lifelong Learning Machines.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Universally Consistent K-Sample Tests via Dependence Measures.

Statistics & probability letters·2024
Same author

Bisected graph matching improves automated pairing of bilaterally homologous neurons from connectomes.

Network neuroscience (Cambridge, Mass.)·2023
Same author

Generative network modeling reveals quantitative definitions of bilateral symmetry exhibited by a whole insect brain connectome.

eLife·2023
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

We developed Integrated Sensing and Processing Decision Trees (ISPDT) to optimize sensor selection for classification tasks. This method minimizes misclassification rates under data constraints, improving classification performance.

Area of Science:

  • Machine Learning
  • Signal Processing
  • Data Science

Background:

  • Adaptive sequential sensing is crucial for classification tasks with limited resources.
  • Optimizing sensor selection directly impacts classification accuracy and efficiency.
  • Existing methods may not adequately address joint optimization of sensing and processing under constraints.

Purpose of the Study:

  • Introduce a novel methodology for adaptive sequential sensing and processing.
  • Optimize sensor selection based on back-end performance metrics like misclassification rate.
  • Address scenarios with sensor or throughput constraints limiting data acquisition.

Main Methods:

  • Propose Integrated Sensing and Processing Decision Trees (ISPDT).
  • Employ a local dimensionality reduction-based partitioning metric for early-stage optimization.

Related Experiment Videos

  • Focus classification tasks at the terminal nodes (leaves) of the decision trees.

    • Main Results:

    • Demonstrate the ISPDT methodology's effectiveness through theoretical analysis.
    • Validate the approach using simulation and experimental results.
    • Showcase optimization of misclassification rate under sensing constraints.

    Conclusions:

    • ISPDT offers an effective framework for adaptive sensing and processing in classification.
    • The methodology successfully optimizes sensor selection to minimize misclassification.
    • The approach is validated across theoretical, simulation, and experimental domains.