TY - JOUR
T1 - An algorithm to optimize explainability using feature ensembles
AU - Lazebnik, Teddy
AU - Bunimovich-Mendrazitsky, Svetlana
AU - Rosenfeld, Avi
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/1
Y1 - 2024/1
N2 - 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 Optimizing Feature Ensembles for Explainability (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.
AB - 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 Optimizing Feature Ensembles for Explainability (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.
KW - Ensemble feature selection
KW - Explainable AI
KW - Machine learning
KW - Optimized feature selection
UR - http://www.scopus.com/inward/record.url?scp=85183688959&partnerID=8YFLogxK
U2 - 10.1007/s10489-023-05069-3
DO - 10.1007/s10489-023-05069-3
M3 - ???researchoutput.researchoutputtypes.contributiontojournal.article???
AN - SCOPUS:85183688959
SN - 0924-669X
VL - 54
SP - 2248
EP - 2260
JO - Applied Intelligence
JF - Applied Intelligence
IS - 2
ER -