Soft Rule Ensembles for Supervised Learning
Deniz Akdemir, Nicolas Heslot, Jean‐Luc Jannink · 2013
In this article supervised learning problems are solved using soft rule ensembles. We first review the importance sampling learning ensembles (ISLE) approach that is useful for generating hard rules. The soft rules are obtained with logistic regression using the corresponding hard rules. Soft rules are useful when both the response and the input variables are continuous because the soft rules provide smooth transitions around the boundaries of a hard rule. Various examples and simulation results show that soft rule ensembles can improve predictive performance over hard rule ensembles.