Logistic Model Trees have been shown to be very accurate and compact classifiers [8]. Their greatest disadvantage is the computational complexity of inducing the logistic regressi...
Abstract. Tree induction methods and linear models are popular techniques for supervised learning tasks, both for the prediction of nominal classes and continuous numeric values. F...
In order to better protect and conserve biodiversity, ecologists use machine learning and statistics to understand how species respond to their environment and to predict how they...
This paper proposes a fault-prone module prediction method that combines association rule mining with logistic regression analysis. In the proposed method, we focus on three key m...
In this paper we propose a new algorithm for learning polyhedral classifiers. In contrast to existing methods for learning polyhedral classifier which solve a constrained optimiza...