Abstract. Tree induction methods and linear models are popular techniques for supervised learning tasks, both for the prediction of nominal classes and numeric values. For predicting numeric quantities, there has been work on combining these two schemes into 'model trees', i.e. trees that contain linear regression functions at the leaves. In this paper, we present an algorithm that adapts this idea for classification problems, using logistic regression instead of linear regression. We use a stagewise fitting process to construct the logistic regression models that can select relevant attributes in the data in a natural way, and show how this approach can be used to build the logistic regression models at the leaves by incrementally refining those constructed at higher levels in the tree. We compare the performance of our algorithm to several other state-of-the-art learning schemes on 36 benchmark UCI datasets, and show that it produces accurate and compact classifiers.
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. For predicting numeric quantities, there has been work on combining these two schemes into 'model trees', i.e. trees that contain linear regression functions at the leaves. In this paper, we present an algorithm that adapts this idea for classification problems, using logistic regression instead of linear regression. We use a stagewise fitting process to construct the logistic regression models that can select relevant attributes in the data in a natural way, and show how this approach can be used to build the logistic regression models at the leaves by incrementally refining those constructed at higher levels in the tree. We compare the performance of our algorithm against that of decision trees and logistic regression on 32 benchmark UCI datasets, and show that it achieves a higher classification accuracy on average than the other two methods.
After nearly two decades of subsidized and energy crop-oriented development, agricultural biogas production in Germany is standing at a crossroads. Fundamental challenges need to be met. In this article we sketch a vision of a future agricultural biogas plant that is an integral part of the circular bioeconomy and works mainly on the base of residues. It is flexible with regard to feedstocks, digester operation, microbial communities and biogas output. It is modular in design and its operation is knowledge-based, information-driven and largely automated. It will be competitive with fossil energies and other renewable energies, profitable for farmers and plant operators and favorable for the national economy. In this paper we discuss the required contribution of research to achieve these aims.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.