The analysis of an algorithm by Koda and Ruskey for listing ideals in a forest poset leads to a study of random binary trees and their limits as infinite random binary trees. The corresponding finite and infinite random forests are studied too. The infinite random binary trees and forests studied here have exactly one infinite path; they can be defined using suitable size-biazed Galton–Watson p...

Trees are a valuable way of displaying structure in datasets, especially for classification problems. Improved classification results can be achieved using forests of trees. Adding various visualization methods and interactive tools for analysis of individual trees and of whole forests gives complementary insight into the data. This paper describes different views and methods to analyze tree fo...

In this article we introduce Random Survival Forests, an ensemble tree method for the analysis of right censored survival data. As is well known, constructing ensembles from base learners, such as trees, can significantly improve learning performance. Recently, Breiman showed that ensemble learning can be further improved by injecting randomization into the base learning process, a method calle...

Some automobile insurance companies use computerized auto-detection systems to expedite claims payment decisions for insured vehicles. Claims suspected of fraud are evaluated using empirical data from previously investigated claims. The main objective this manuscript is demonstrate a novel processing system and its potential in classification. approach was used develop machine learning-based se...

INTRODUCTION The prediction of plasma protein binding (ppb) is of paramount importance in the pharmacokinetics characterization of drugs, as it causes significant changes in volume of distribution, clearance and drug half life. This study utilized Quantitative Structure - Activity Relationships (QSAR) for the prediction of plasma protein binding. METHODS Protein binding values for 794 compoun...