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Feature-Based Complexity Measure for Multinomial Classification Datasets.

Kyle Erwin1, Andries Engelbrecht1,2,3

  • 1Computer Science Division, Stellenbosh University, Stellenbosch 7600, South Africa.

Entropy (Basel, Switzerland)
|July 29, 2023
PubMed
Summary

This study introduces the F5 measure, a new feature-based complexity metric for machine learning classification. The F5 measure accurately assesses dataset complexity, outperforming existing methods, especially for multi-class problems.

Keywords:
classification problem complexityfeature-based complexity measuresmultinomial classification datasetssynthetic datasets

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Area of Science:

  • Computer Science
  • Machine Learning
  • Data Mining

Background:

  • Machine learning classification on tabular data relies on understanding dataset complexity.
  • Feature-based complexity measures assess feature utility for class discrimination.
  • Existing measures inadequately capture complexity, particularly in multi-class datasets.

Purpose of the Study:

  • To address limitations in existing feature-based complexity measures.
  • To propose a novel feature-based complexity measure, the F5 measure.
  • To evaluate the effectiveness of the F5 measure on synthetic classification datasets.

Main Methods:

  • Development of the F5 measure, evaluating feature discriminative power per class.
  • Identification of long sequences of uninterrupted instances of the same class.
  • Comparative analysis of the F5 measure against existing feature-based complexity measures.

Main Results:

  • Existing feature-based complexity measures demonstrate inadequacy for certain synthetic datasets, especially multi-class ones.
  • The proposed F5 measure effectively evaluates feature complexity by analyzing class-specific instance sequences.
  • The F5 measure provides a more accurate representation of dataset feature complexity.

Conclusions:

  • The F5 measure offers improved accuracy in assessing feature-based complexity for classification datasets.
  • This new measure is particularly beneficial for understanding multi-class classification challenges.
  • The F5 measure facilitates better model selection and design in machine learning.