Generating Decision Trees from Otoneurological Data with a Variable Grouping Method

Authors:
Kati Viikki;Erna Kentala;Martti Juhola;Ilmari Pyykkö/;Pekka Honkavaara
Affiliations:
Department of Computer and Information Sciences, FIN-33014 University of Tampere, Tampere, Finland/ kati.viikki@cs.uta.fi;Department of Otorhinolaryngology, Helsinki University Central Hospital, Helsinki, Finland;Department of Computer and Information Sciences, FIN-33014 University of Tampere, Tampere, Finland;Department of Otorhinolaryngology, Karolinska Hospital, Stockholm, Sweden;Department of Anaesthesia, Military Central Hospital, Helsinki, Finland
Venue:
Journal of Medical Systems
Year:
2002

Citing 4
Cited 0

Selection of relevant features and examples in machine learning

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Wrappers for feature subset selection

Artificial Intelligence - Special issue on relevance
Data Structures and Algorithms

Data Structures and Algorithms
Induction of Decision Trees

Machine Learning

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Abstract

When medical data sets are modelled by machine learning methods, wealth of variables may be available. This paper deals with variable selection for decision tree induction in the context of two otoneurological data sets: vertigo data, and postoperative nausea and vomiting data. First, a variable grouping method based on measures of association and graph theoretic techniques was used to gain insight into data. Then, representations of learning data were defined using the information from discovered variable groups, and decision trees were generated. The use of variable grouping method was beneficial by revealing interesting associations between variables and enabling generation of accurate and reasonable decision trees that modelled the application areas from different viewpoints.