Abstract Decision Trees (DTs) are predictive models in supervised learning, known not only
for their unquestionable utility in a wide range of applications but also for their interpretability …

A rapid growth of interest in rough set theory [297] and its applications can be lately seen in
the number of international workshops, conferences and seminars that are either directly …

State-of-the-art decision tree methods apply heuristics recursively to create each split in
isolation, which may not capture well the underlying characteristics of the dataset. The …

Decision forests (also known as random forests) are an indispensable tool for automatic
image analysis. This practical and easy-to-follow text explores the theoretical underpinnings …

Decision trees are attractive classifiers due to their high execution speed. But trees derived
with traditional methods often cannot be grown to arbitrary complexity for possible loss of …

Much of previous attention on decision trees focuses on the splitting criteria and optimization
of tree sizes. The dilemma between overfitting and achieving maximum accuracy is seldom …

Unlike a univariate decision tree, a multivariate decision tree is not restricted to splits of the
instance space that are orthogonal to the features' axes. This article addresses several …

Induced decision trees are an extensively-researched solution to classi cation tasks. For
many practical tasks, the trees produced by tree-generation algorithms are not …

The papers on rough set theory and its applications placed in this volume present a wide
spectrum of problems representative to the present. stage of this theory. Researchers from …

The classification problem can be addressed by numerous techniques and algorithms which
belong to different paradigms of machine learning. In this paper, we are interested in …