The main objective of the present study was to provide a novel methodological approach for
flash flood susceptibility modeling based on a feature selection method (FSM) and tree …

Machine learning has been used successfully to improve the accuracy of computer-aided
diagnosis systems. This paper experimentally assesses the performance of models derived …

Quantum Machine Learning bridges the gap between abstract developments in quantum
computing and the applied research on machine learning. Paring down the complexity of the …

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 …

Methods for voting classification algorithms, such as Bagging and AdaBoost, have been
shown to be very successful in improving the accuracy of certain classifiers for artificial and …

This article reports an empirical investigation of the accuracy of rules that classify examples
on the basis of a single attribute. On most datasets studied, the best of these very simple …

Many supervised machine learning algorithms require a discrete feature space. In this
paper, we review previous work on continuous feature discretization, identify defining …

Classification algorithm selection is an important issue in many disciplines. Since it normally
involves more than one criterion, the task of algorithm selection can be modeled as multiple …

In this paper we present an average-case analysis of the Bayesian classi er, a simple
probabilistic induction algorithm that fares remarkably well on many learning tasks. Our …

Learning from outliers and imbalanced data remains one of the major difficulties for machine
learning classifiers. Among the numerous techniques dedicated to tackle this problem, data …