Prediction of Ordinal Classes Using Regression Trees View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2002-07-02

AUTHORS

Stefan Kramer , Gerhard Widmer , Bernhard Pfahringer , Michael de Groeve

ABSTRACT

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. More... »

PAGES

426-434

References to SciGraph publications

  • 1999. A Decision Tree Algorithm for Ordinal Classification in ADVANCES IN INTELLIGENT DATA ANALYSIS
  • 1999. Experiments in Predicting Biodegradability in INDUCTIVE LOGIC PROGRAMMING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/3-540-39963-1_45

    DOI

    http://dx.doi.org/10.1007/3-540-39963-1_45

    DIMENSIONS

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