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Classification of Systems-II01:31

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Related Experiment Video

Updated: Apr 4, 2026

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

8.1K

A Minimax Framework for Classification with Applications to Images and High Dimensional Data.

Qiang Cheng, Hongbo Zhou, Jie Cheng

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 10, 2015
    PubMed
    Summary

    This study presents a new minimax classification framework for high-dimensional data. This robust approach minimizes errors under distortions, outperforming existing methods like support vector machines in accuracy.

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    Last Updated: Apr 4, 2026

    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

    8.1K

    Area of Science:

    • Machine Learning
    • Computer Vision
    • Bioinformatics

    Background:

    • Multiclass classification is crucial for high-dimensional data analysis.
    • Existing methods may struggle with complex distortions and nonlinearities.
    • Robustness to data variations is a key challenge in classification.

    Purpose of the Study:

    • Introduce a novel minimax framework for multiclass classification.
    • Develop a versatile framework applicable to diverse data types, including imagery.
    • Enhance classification accuracy by minimizing errors under specific distortions.

    Main Methods:

    • Estimate a representation model minimizing fitting errors under distortions.
    • Derive categorical information from the estimated model.
    • Incorporate kernel techniques for handling nonlinear input spaces.
    • Introduce minimax classification with generalized multiplicative distortions for signal-dependent distortions.

    Main Results:

    • The proposed framework accommodates various regression models (e.g., lasso, elastic net, ridge) as special cases.
    • Optimal decision rules were derived for the minimax classification framework.
    • A new family of classifiers, minimax classification with generalized multiplicative distortions, was developed.
    • This new family often surpasses state-of-the-art methods like Support Vector Machines in accuracy.
    • Experimental validation on image and gene expression data confirms framework effectiveness.

    Conclusions:

    • The minimax framework offers a powerful and flexible approach to multiclass classification.
    • The novel generalized multiplicative distortions classifiers demonstrate superior performance.
    • The framework's adaptability and effectiveness are validated across diverse datasets.