An algorithm to optimize explainability using feature ensembles

نتاج البحث: نشر في مجلةمقالةمراجعة النظراء

4 اقتباسات (Scopus)

ملخص

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.

اللغة الأصليةالإنجليزيّة
الصفحات (من إلى)2248-2260
عدد الصفحات13
دوريةApplied Intelligence
مستوى الصوت54
رقم الإصدار2
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - يناير 2024

بصمة

أدرس بدقة موضوعات البحث “An algorithm to optimize explainability using feature ensembles'. فهما يشكلان معًا بصمة فريدة.

قم بذكر هذا