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An Efficient Hybrid Feature Selection Method Using the Artificial Immune Algorithm for High-Dimensional Data.

Yongbin Zhu^1,2, Tao Li¹, Wenshan Li¹

¹College of Cybersecurity, Sichuan University, Chengdu 610065, China.

Computational Intelligence and Neuroscience

|October 24, 2022

Summary

This summary is machine-generated.

A novel hybrid feature selection method (HFIA) optimizes high-dimensional data mining by balancing subset quality and computational cost. HFIA significantly improves classification accuracy and achieves over 99% feature reduction.

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Area of Science:

Data Mining
Machine Learning
Computational Intelligence

Background:

Feature selection is crucial for optimizing data mining models.
High-dimensional data presents challenges in balancing feature subset quality with computational efficiency.

Purpose of the Study:

To propose an efficient hybrid feature selection method (HFIA) for high-dimensional data.
To improve the balance between feature subset quality and computational cost in feature selection.

Main Methods:

Developed HFIA, integrating filter algorithms with an improved artificial immune algorithm.
Incorporated lethal mutation and Cauchy operator for enhanced search performance.
Introduced an adaptive adjustment factor in mutation and update phases.

Main Results:

HFIA achieved a feature reduction rate exceeding 99% across 25 benchmark datasets.
Classifier performance improved by 5% to 48.33%.
HFIA demonstrated superior performance compared to 19 state-of-the-art methods in accuracy, reduction rate, and computational cost.

Conclusions:

HFIA offers a superior approach to feature selection for high-dimensional data.
The method effectively enhances classifier performance while reducing computational burden.
HFIA represents a significant advancement in efficient and effective feature selection.