Decision Trees with Hypotheses

Decision trees are widely used in many areas of computer science and related fields as classifiers, as a means for knowledge representation, and as algorithms to solve various problems. They are studied, in particular, in test theory, rough set theory, and exact learning. These theories are closely related: attributes from rough set theory and test theory correspond to membership queries from exact learning. Exact learning studies additionally the so-called equivalence queries.
In this book, we added to the model considered in test theory and rough set theory the notion of a hypothesis that allowed us to use an analog of equivalence queries and studied decision trees using various combinations of attributes, hypotheses, and proper hypotheses (an analog of proper equivalence queries). The two main goals of this book are (i) to create tools for the experimental and theoretical study of decision trees with hypotheses …