A new definition for feature selection stability analysis

Teddy Lazebnik, Avi Rosenfeld

نتاج البحث: نشر في مجلةمقالةمراجعة النظراء

1 اقتباس (Scopus)

ملخص

Feature selection (FS) stability is an important topic of recent interest. Finding stable features is important for creating reliable, non-overfitted feature sets, which in turn can be used to generate machine learning models with better accuracy and explanations and are less prone to adversarial attacks. There are currently several definitions of FS stability that are widely used. In this paper, we demonstrate that existing stability metrics fail to quantify certain key elements of many datasets such as resilience to data drift or non-uniformly distributed missing values. To address this shortcoming, we propose a new definition for FS stability inspired by Lyapunov stability in dynamic systems. We show the proposed definition is statistically different from the classical record-stability on (n=90) datasets. We present the advantages and disadvantages of using Lyapunov and other stability definitions and demonstrate three scenarios in which each one of the three proposed stability metrics is best suited.

اللغة الأصليةالإنجليزيّة
الصفحات (من إلى)753-770
عدد الصفحات18
دوريةAnnals of Mathematics and Artificial Intelligence
مستوى الصوت92
رقم الإصدار3
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - يونيو 2024

بصمة

أدرس بدقة موضوعات البحث “A new definition for feature selection stability analysis'. فهما يشكلان معًا بصمة فريدة.

قم بذكر هذا