Prediction of Ordinal Classes Using Regression Trees

• Autorzy: Kramer, S. , Widmer, G. , Pfahringer, B. , De Groeve, M.
• Języki publikacji: EN

Abstrakty

EN

This paper is devoted to the problem of learning to predict ordinal (i.e., ordered discrete) classes using classification and regression trees. We start with S-CART, a tree induction algorithm, and study various ways of transforming it into a learner for ordinal classification tasks. These algorithm variants are compared on a number of benchmark data sets to verify the relative strengths and weaknesses of the strategies and to study the trade-off between optimal categorical classification accuracy (hit rate) and minimum distance-based error. Preliminary results indicate that this is a promising avenue towards algorithms that combine aspects of classification and regression.

Słowa kluczowe

EN

machine learning; ordinal classes; regression trees; decision tree; classification; regression; inductive logic programming

• Czasopismo: Fundamenta Informaticae
• Rocznik: 2001
• Tom: Vol. 47, nr 1,2
• Strony: 1-13
• Opis fizyczny: tab., wykr., bibliogr. 23 poz.

Twórcy

autor

Kramer, S.

autor

Widmer, G.

autor

Pfahringer, B.

autor

De Groeve, M.

• Institute for Computer Science. Albert-Ludwigs-University Freiburg, Georges-Köhler-Allee Geb. 79, D-79110 Freiburg i.Br.,Germany,