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Partitioning methods for classification and decision making in medicine
Author(s) -
Marshall R. J.
Publication year - 1986
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4780050516
Subject(s) - partition (number theory) , class (philosophy) , computer science , recursive partitioning , multiclass classification , data mining , artificial intelligence , statistics , machine learning , mathematics , support vector machine , combinatorics
The use of partitioning methods for classification is discussed. A brief outline of the method of recursive partitioning is given and a note is made of some of its potential drawbacks. An alternative approach is outlined in which a particular class of dichotomous partitions is considered. The class incorporates prior knowledge concerning the nature of an association. Strategies to choose a partition from this class are suggested. The method is illustrated by an analysis of data from patients with gastrointestinal cancer and benign disease. A partition to discriminate between cancer and benign disease is obtained using symptoms, age and tumour marker data.