Finding the right decision tree's induction strategy for a hard real world problem.

Abstract:

:Decision trees have been already successfully used in medicine, but as in traditional statistics, some hard real world problems can not be solved successfully using the traditional way of induction. In our experiments we tested various methods for building univariate decision trees in order to find the best induction strategy. On a hard real world problem of the Orthopaedic fracture data with 2637 cases, described by 23 attributes and a decision with three possible values, we built decision trees with four classical approaches, one hybrid approach where we combined neural networks and decision trees, and with an evolutionary approach. The results show that all approaches had problems with either accuracy, sensitivity, or decision tree size. The comparison shows that the best compromise in hard real world problem decision trees building is the evolutionary approach.

journal_name

Int J Med Inform

authors

Zorman M,Podgorelec V,Kokol P,Peterson M,Sprogar M,Ojstersek M

doi

10.1016/s1386-5056(01)00176-9

subject

Has Abstract

pub_date

2001-09-01 00:00:00

pages

109-21

issue

1-2

eissn

1386-5056

issn

1872-8243

pii

S1386-5056(01)00176-9

journal_volume

63

pub_type

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