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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

A high utility itemsets mining algorithm based on co-evolution.

Wenyan Yang¹, Meng Han¹, Zhenlong Dai¹

¹School of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, China.

Scientific Reports

|December 3, 2025

Summary

This summary is machine-generated.

This study introduces a novel co-evolutionary algorithm to improve high utility itemsets mining. The new method enhances diversity and efficiency, outperforming existing techniques in finding valuable itemsets.

Keywords:

Adaptive simplified mutation Co-evolution Cuckoo search High utility itemsets Population initialization strategy

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Area of Science:

Data Mining
Artificial Intelligence
Optimization Algorithms

Background:

Metaheuristic algorithms for high utility itemsets mining (HUIM) struggle with initial population quality, efficiency, and premature convergence.
These limitations hinder the effective discovery of valuable patterns in large datasets.

Purpose of the Study:

To propose a novel co-evolutionary algorithm for high utility itemsets mining.
To enhance population diversity, improve initial solution quality, and boost search efficiency.

Main Methods:

A co-evolutionary approach integrating particle swarm optimization with cuckoo search's Lévy flight mechanism.
Population initialization using logarithmic decay and probability distribution.
An adaptive simplified mutation strategy to prevent premature convergence and itemset loss.

Main Results:

The proposed algorithm significantly outperforms state-of-the-art methods.
Demonstrated improvements in the number of high utility itemsets discovered.
Enhanced performance in terms of runtime, recall, precision, and convergence speed.

Conclusions:

The co-evolutionary strategy effectively addresses the limitations of traditional metaheuristic HUIM algorithms.
The proposed method offers a more robust and efficient solution for discovering high utility itemsets.