Decision tree techniques have been widely used to build classification models as such
models closely resemble human reasoning and are easy to understand. This paper …

Fifty years have passed since the publication of the first regression tree algorithm. New
techniques have added capabilities that far surpass those of the early methods. Modern …

A unified, coherent treatment of current classifier ensemble methods, from fundamentals of
pattern recognition to ensemble feature selection, now in its second edition The art and …

Geohazard prevention and mitigation are highly complex and remain challenges for
researchers and practitioners. Artificial intelligence (AI) has become an effective tool for …

The growing global demands for agricultural goods will require accelerated crop
improvement. High-throughput genomic, phenomic, enviromic and other multi-omic data …

We discuss a novel decision tree architecture with soft decisions at the internal nodes where
we choose both children with probabilities given by a sigmoid gating function. Our algorithm …

A novel linear multivariate decision tree classifier, Binary Decision Tree based on K-means
Splitting (BDTKS), is presented in this paper. The unsupervised K-means clustering is …

Presents a detailed study of the major design components that constitute a top-down
decision-tree induction algorithm, including aspects such as split criteria, stop** criteria …

We propose a combined fusion-selection approach to classifier ensemble design. Each
classifier in the ensemble is replaced by a miniensemble of a pair of subclassifiers with a …

We study the problem of formally and automatically verifying robustness properties of
decision tree ensemble classifiers such as random forests and gradient boosted decision …