An algorithm to optimize explainability using feature ensembles

Feature Ensembles are a robust and effective method for finding the feature set that yields the best predictive accuracy for learning agents. However, current feature ensemble algorithms do not consider explainability as a key factor in their construction. To address this limitation, we present an a...

Full description

Saved in:
Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Vol. 54; no. 2; pp. 2248 - 2260
Main Authors: Lazebnik, Teddy, Bunimovich-Mendrazitsky, Svetlana, Rosenfeld, Avi
Format: Journal Article
Language:English
Published: New York Springer US 01.01.2024
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Computer Science
Datasets
Machines
Manufacturing
Mechanical Engineering
Optimization
Processes
Stability
Explainable AI
Ensemble feature selection
Optimized feature selection
Machine learning
ISSN:0924-669X, 1573-7497
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Feature Ensembles are a robust and effective method for finding the feature set that yields the best predictive accuracy for learning agents. However, current feature ensemble algorithms do not consider explainability as a key factor in their construction. To address this limitation, we present an algorithm that optimizes for the explainability and performance of a model – the O ptimizing F eature E nsembles for E xplainability (OFEE) algorithm. OFEE uses intersections of feature sets to produce a feature ensemble that optimally balances explainability and performance. Furthermore, OFEE is parameter-free and as such optimizes itself to a given dataset and explainability requirements. To evaluated OFEE, we considered two explainability measures, one based on ensemble size and the other based on ensemble stability. We found that OFEE was overall extremely effective within the nine canonical datasets we considered. It outperformed other feature selection algorithms by an average of over 8% and 7% respectively when considering the size and stability explainability measures.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-023-05069-3