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Related Concept Videos

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Related Experiment Video

Updated: Feb 22, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Enhancing Multimedia Imbalanced Concept Detection Using VIMP in Random Forests.

Saad Sadiq1, Yilin Yan1, Mei-Ling Shyu1

  • 1Department of Electrical and Computer Engineering University of Miami, Coral Gables, FL, USA.

Proceedings of the ... IEEE International Conference on Information Reuse and Integration. IEEE International Conference on Information Reuse and Integration
|September 21, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel random forest framework to improve multimedia big data concept detection, especially for rare concepts. The new method enhances information retrieval accuracy and efficiency in managing large datasets.

Keywords:
Multimedia imbalanced concept detectionMultivariate regressionRandom forestsVariable importance (VIMP)

Related Experiment Videos

Last Updated: Feb 22, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

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Area of Science:

  • Computer Science
  • Data Science
  • Information Retrieval

Background:

  • Exponential growth in multimedia data from social media and cloud storage presents challenges in data management and retrieval.
  • Current content-based concept detection methods struggle to bridge the semantic gap, hindering effective information retrieval from big data.

Purpose of the Study:

  • To develop an advanced framework for concept detection in multimedia big data.
  • To address the limitations of existing methods in handling rare and imbalanced concepts.
  • To improve the accuracy and efficiency of information retrieval from large-scale multimedia datasets.

Main Methods:

  • A multi-stage random forest framework was developed.
  • Predictor variables were generated using multivariate regressions with variable importance (VIMP).
  • The framework was fine-tuned, reducing predictor variables and evaluating concept detection for rare and imbalanced data.

Main Results:

  • The proposed framework demonstrated superior performance compared to existing approaches.
  • Improvements were particularly notable in scenarios with rare and imbalanced concepts.
  • The method achieved higher Mean Average Precision (MAP) values, indicating enhanced retrieval accuracy.

Conclusions:

  • The multi-stage random forest framework effectively bridges the semantic gap in multimedia big data.
  • The approach provides a robust solution for concept detection, even with rare or imbalanced data.
  • This work advances information retrieval techniques for managing and accessing large multimedia archives.