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A Pareto chart is a bar graph or a combination of both line and bar graphs. The bar lengths represent the individual values or the frequency, while the lines represent the cumulative total values. In this chart, the longest bars are arranged on the left and the shortest bars on the right, which makes it easier to read and interpret the data. It can also be called a Pareto diagram or Pareto analysis.
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Margin-Based Pareto Ensemble Pruning: An Ensemble Pruning Algorithm That Learns to Search Optimized Ensembles.

Ruihan Hu1,2, Songbin Zhou1, Yisen Liu1

  • 1Guangdong Key Laboratory of Modern Control Technology, Guangdong Institute of Intelligent Manufacturing, Guangdong Academy of Sciences, Guangzhou 510070, Guangdong Province, China.

Computational Intelligence and Neuroscience
|July 9, 2019
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Summary
This summary is machine-generated.

This study introduces margin-based Pareto ensemble pruning, a dynamic machine learning framework. It optimizes ensemble size and prunes experts for improved classification performance and smaller model sizes.

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Area of Science:

  • Machine Learning
  • Computer Science
  • Artificial Intelligence

Background:

  • Ensemble pruning systems classify data by selecting competent learners from a pool.
  • Current systems often use fixed ensemble pool sizes and struggle with defining optimal regions of competence.

Purpose of the Study:

  • To propose a dynamic pruning framework, margin-based Pareto ensemble pruning, for ensemble pruning systems.
  • To optimize ensemble pool size and fine-tune experts for enhanced classification performance.

Main Methods:

  • Utilizes Pareto optimization to explore optimal ensemble pool sizes during overproduction.
  • Employs margin criterion pruning, considering information entropy in the indecision region, to prune less effective experts.
  • Evaluates the framework on various datasets for classification tasks.

Main Results:

  • The proposed margin-based Pareto ensemble pruning achieves smaller ensemble sizes compared to existing models.
  • Demonstrates superior classification performance across most tested datasets.
  • Effectively prunes experts based on their competence with respect to the test set.

Conclusions:

  • Margin-based Pareto ensemble pruning offers a more effective approach to ensemble selection.
  • The dynamic framework balances ensemble size and classification accuracy.
  • Provides a significant advancement over traditional, static ensemble pruning methods.