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Related Experiment Video

Updated: Apr 1, 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

Budget constrained non-monotonic feature selection.

Haiqin Yang¹, Zenglin Xu², Michael R Lyu¹

¹Shenzhen Key Laboratory of Rich Media Big Data Analytics and Application, Shenzhen Research Institute, The Chinese University of Hong Kong,; Computer Science & Engineering, The Chinese University of Hong Kong, Hong Kong.

Neural Networks : the Official Journal of the International Neural Network Society

|October 4, 2015

Summary

This summary is machine-generated.

This study introduces a novel algorithm for non-monotonic feature selection, overcoming limitations of traditional methods. The approach uses Multiple Kernel Learning (MKL) to efficiently select optimal feature subsets under budget constraints.

Keywords:

Budget constraint Feature selection Multiple kernel learning Non-monotonic

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

Computer Science
Machine Learning
Data Mining

Background:

Feature selection is crucial in machine learning and data mining.
Traditional methods exhibit a monotonic property, limiting effectiveness for non-monotonic feature dependencies.
Budget constraints on feature subset size present a significant challenge.

Purpose of the Study:

To develop an algorithm for non-monotonic feature selection.
To address the limitations of traditional monotonic feature selection methods.
To approximate the combinatorial optimization problem using Multiple Kernel Learning (MKL).

Main Methods:

Developed a novel algorithm for non-monotonic feature selection.
Approximated the combinatorial optimization problem via Multiple Kernel Learning (MKL).
Provided performance guarantees for the derived solution compared to the global optimum.

Main Results:

The proposed framework demonstrates promising performance on synthetic and real-world datasets.
Empirical evaluations were conducted for both classification and regression tasks.
The algorithm effectively handles non-monotonic feature selection under budget constraints.

Conclusions:

The developed MKL-based approach offers an effective solution for non-monotonic feature selection.
The framework outperforms baseline feature selection methods.
This work advances feature selection techniques by addressing non-monotonicity and budget constraints.