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Related Experiment Video

Updated: May 23, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

On prioritized multiple-criteria aggregation.

Ronald R Yager¹

¹Machine Intelligence Institute, Iona College, New Rochelle, NY 10805, USA. yager@panix.com

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|April 12, 2012

Summary

This summary is machine-generated.

This study introduces prioritized aggregation, a method for combining criteria scores where high-priority criteria must be met. It presents two novel approaches for formulating this aggregation process in decision-making.

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Last Updated: May 23, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Decision Sciences
Operations Research
Applied Mathematics

Background:

Multicriteria aggregation is crucial for modern decision-making processes.
Existing methods may not adequately handle situations with strict priority levels.
The need for robust aggregation techniques that respect criterion importance is growing.

Purpose of the Study:

To introduce and define the concept of aggregation imperative.
To present and analyze prioritized aggregation as a specific type of aggregation imperative.
To propose and discuss two distinct mathematical formulations for prioritized aggregation.

Main Methods:

Development of the concept of aggregation imperative.
Formulation of prioritized aggregation using a prioritized aggregation operator.
Formulation of prioritized aggregation using integral-type aggregation with a monotonic set measure.

Main Results:

The aggregation imperative concept provides a framework for understanding how individual criteria combine.
Prioritized aggregation effectively models scenarios where lower-priority criteria cannot compensate for failures in higher-priority ones.
Two distinct, mathematically sound methods for implementing prioritized aggregation are presented and discussed.

Conclusions:

Prioritized aggregation offers a valuable tool for complex decision-making problems with hierarchical criteria.
The proposed methods provide practical approaches for implementing prioritized aggregation in real-world applications.
This work advances the understanding and application of multicriteria aggregation techniques.