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Related Concept Videos

Aggregates Classification01:29

Aggregates Classification

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.
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Tailored aggregation for classification.

Tristan Mary-Huard1, Stéphane Robin

  • 1UMR AgroParisTech/INRIA, Paris Cedex 05, France. maryhuar@agroparistech.fr

IEEE Transactions on Pattern Analysis and Machine Intelligence
|September 19, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces aggregation, a new strategy for handling large datasets in classification by clustering and compressing redundant variables. This approach enhances classifier reliability by leveraging information from these variables.

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Area of Science:

  • Data Science
  • Machine Learning
  • Statistical Modeling

Background:

  • Large-dimension datasets pose challenges for classification.
  • Classical strategies include compression and variable selection.
  • Redundant variables can be informative but difficult to utilize.

Purpose of the Study:

  • To propose aggregation as an alternative strategy for large-dimension data classification.
  • To develop a statistical framework for tailored aggregation methods.
  • To integrate aggregation with variable selection for improved classifier performance.

Main Methods:

  • Aggregation involves clustering redundant variables and compressing within groups.
  • A statistical framework is developed for defining aggregation methods.
  • Two algorithms are proposed for ordered and nonordered variables.
  • Applications are demonstrated using kNN and CART algorithms.

Main Results:

  • Aggregation effectively utilizes information from redundant variables.
  • The proposed methods enhance classifier reliability.
  • Tailored aggregation combined with selection methods yields robust classifiers.

Conclusions:

  • Aggregation offers a novel and effective approach to classification with high-dimensional data.
  • The statistical framework supports the development of customized aggregation techniques.
  • This strategy complements existing methods, improving the handling of redundant variables.