Polynomial-fuzzy decision tree structures for classifying medical data
Mugambe, E.M., Hunter, Andrew, Oatley, Giles, and Kennedy, Lee (2004) Polynomial-fuzzy decision tree structures for classifying medical data. Knowledge Based Systems, 17 (2). pp. 81-87.
|PDF (Published Version) - Repository staff only - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader|
View at Publisher Website: http://dx.doi.org/10.1016/j.knosys.2004....
Decision tree induction has been studied extensively in machine learning as a solution for classification problems. The way the linear decision trees partition the search space is found to be comprehensible and hence appealing to data modelers. Comprehensibility is an important aspect of models used in medical data mining as it determines model credibility and even acceptability. In the practical sense though, inordinately long decision trees compounded by replication problems detracts from comprehensibility. This demerit can be partially attributed to their rigid structure that is unable to handle complex non-linear or/and continuous data. To address this issue we introduce a novel hybrid multivariate decision tree composed of polynomial, fuzzy and decision tree structures. The polynomial nature of these multivariate trees enable them to perform well in non-linear territory while the fuzzy members are used to squash continuous variables. By trading-off comprehensibility and performance using a multi-objective genetic programming optimization algorithm, we can induce polynomial-fuzzy decision trees (PFDT) that are smaller, more compact and of better performance than their linear decision tree (LDT) counterparts. In this paper we discuss the structural differences between PFDT and LDT (C4.5) and compare the size and performance of their models using medical data.
|Item Type:||Article (Refereed Research - C1)|
|Keywords:||decision tree; comprehensibility; performance; multiobjective genetic programming|
|FoR Codes:||11 MEDICAL AND HEALTH SCIENCES > 1199 Other Medical and Health Sciences > 119999 Medical and Health Sciences not elsewhere classified @ 100%|
|SEO Codes:||92 HEALTH > 9299 Other Health > 929999 Health not elsewhere classified @ 100%|
|Deposited On:||05 May 2010 16:16|
|Last Modified:||12 Feb 2011 22:55|
Last 12 Months: 0
Repository Staff Only: item control page