.png){kind=icon}
.png){kind=icon}
Return
Estimating Variable Importance and Interaction in Machine Learning via Genetic Algorithms
.png){kind=icon}
PRE
AI
DOI：10.1007/978-3-032-15987-8_32.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This work explores the use of the Genetic Algorithm with Linkage Learning (GAwLL) for the feature selection problem. A notable byproduct of GAwLL application for feature selection is the generation of a variable interaction graph, offering insights into feature dependencies. To extend the original C++ implementation which was limited to the K-nearest neighbors (KNN) algorithm we introduce PyGAwLLfs, a Python-based framework designed to support feature selection for a wide range of machine learning models. Using PyGAwLLfs, we assess GAwLL performance across various models, including decision trees, random forests, and artificial neural networks. Furthermore, based on the internal mechanisms of GAwLL, we present a novel method for estimating variable importance in machine learning. Experimental evaluation on multiple datasets, including one from particle physics, demonstrates PyGAwLLfs effectiveness in the estimation of variable importance and interaction. We compare the variable importance scores generated by PyGAwLLfs with those produced by traditional approaches.
Keywords:
Genetic Algorithm
Feature Selection
Variable Importance
Machine Learning
Interaction Graph
Journal
I
IF:
0
Papers:
41
Citations:
0