Novel and Efficient Randomized Algorithms for Feature Selection
Feature selection is a crucial problem in efficient machine learning, and it also greatly contributes to the explainability of machine-driven decisions. Methods, like decision trees and Least Absolute Shrinkage and Selection Operator (LASSO), can select features during training. However, these embed...
Saved in:
Main Authors: | Zigeng Wang, Xia Xiao, Sanguthevar Rajasekaran |
---|---|
Format: | Article |
Language: | English |
Published: |
Tsinghua University Press
2020-09-01
|
Series: | Big Data Mining and Analytics |
Subjects: | |
Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2020.9020005 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
An Enhanced Sine Cosine Algorithm for Feature Selection in Network Intrusion Detection
by: zahra asgari varzaneh, et al.
Published: (2024-12-01) -
Binary plant rhizome growth-based optimization algorithm: an efficient high-dimensional feature selection approach
by: Jin Zhang, et al.
Published: (2025-01-01) -
<i>VFL-Cafe</i>: Communication-Efficient Vertical Federated Learning via Dynamic Caching and Feature Selection
by: Jiahui Zhou, et al.
Published: (2025-01-01) -
A novel group-based framework for nature-inspired optimization algorithms with adaptive movement behavior
by: Adam Robson, et al.
Published: (2025-01-01) -
Endpoint carbon content and temperature prediction model in BOF steelmaking based on posterior probability and intra-cluster feature weight online dynamic feature selection
by: Wang Haodong, et al.
Published: (2025-01-01)