A new representation in genetic programming with hybrid feature ranking criterion for high-dimensional feature selection
Abstract Feature selection is a common method for improving classification performance. Selecting features for high-dimensional data is challenging due to the large search space. Traditional feature ranking methods that search for top-ranked features cannot remove redundant and irrelevant features a...
Saved in:
| Main Authors: | Jiayi Li, Fan Zhang, Jianbin Ma |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-02-01
|
| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-025-01784-1 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Precision Measurement and Feature Selection in Medical Diagnostics using Hybrid Genetic Algorithm and Support Vector Machine
by: Gowri Subadra K, et al.
Published: (2025-07-01) -
An efficient and interactive feature selection approach based on copula entropy for high-dimensional genetic data
by: Xiaoran Yan, et al.
Published: (2025-08-01) -
Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications
by: Duarte Coelho, et al.
Published: (2025-07-01) -
Dynamic Multi-Level Competition Learning-Based Dual-Task Optimization for High-Dimensional Feature Selection
by: Weiwei Zhang, et al.
Published: (2024-01-01) -
Evolving Many-Objective Job Shop Scheduling Dispatching Rules via Genetic Programming With Adaptive Search Based on the Frequency of Features
by: Atiya Masood, et al.
Published: (2025-01-01)