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Spherical model for Minimalist Machine Learning paradigm in handling complex databases.

Raúl Jimenez-Cruz1,2, Cornelio Yáñez-Márquez2, Miguel Gonzalez-Mendoza1

  • 1Tecnologico de Monterrey, School of Engineering and Sciences, Monterrey, Mexico.

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Summary
This summary is machine-generated.

The N-Spherical Minimalist Machine Learning (MML) classifier tackles high-dimensional and imbalanced data. This novel approach shows superior efficiency and robustness for binary classification tasks.

Keywords:
Minimalist Machine Learningclassificationmachine learningpattern classificationpattern recognition

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Area of Science:

  • Machine Learning
  • Data Science
  • Computational Statistics

Background:

  • Minimalist Machine Learning (MML) offers a simplified approach to complex algorithms.
  • High-dimensional and imbalanced datasets pose significant challenges for traditional classifiers.
  • Existing methods often struggle with efficiency and robustness on such data.

Purpose of the Study:

  • To introduce the N-Spherical Minimalist Machine Learning (MML) classifier.
  • To address data dimensionality and class imbalance issues in machine learning classification.
  • To evaluate the performance and robustness of the proposed MML classifier.

Main Methods:

  • Development of a novel classifier utilizing N-spherical coordinates.
  • Integration of metaheuristics and associative models within the MML framework.
  • Performance evaluation using F1 measure and balanced accuracy metrics.
  • Statistical validation through Friedman and Holm tests.

Main Results:

  • The N-Spherical MML classifier demonstrated superior efficiency and robustness.
  • The model effectively handled challenges of high dimensionality and class imbalance.
  • Comparative analysis showed advantages over state-of-the-art classifiers.
  • Statistical tests confirmed the significance of the results.

Conclusions:

  • Minimalist approaches show significant potential for classifying complex datasets.
  • The N-Spherical MML classifier is a promising tool for binary classification tasks.
  • Future work will focus on extending the model to multi-class problems and categorical data handling.