Comparison of crisp and fuzzy classification trees using gini index impurity measure on simulated data

Simple item page
dc.contributor.authorMuchai, Eunice
dc.contributor.authorOdongo, Leo
dc.date.accessioned2015-02-09T13:30:21Z
dc.date.available2015-02-09T13:30:21Z
dc.date.issued2015-02-09
dc.description.abstractCrisp classification trees have been used to model many situations such as disease classification. With the introduction of fuzzy theory, fuzzy classification trees are gaining popularity especially in data mining. Very little work has been done in comparing crisp and fuzzy classification trees. This paper compares crisp classification trees and fuzzy classification trees using Gini index as the impurity measure. The objective is to determine which of the two classification trees gives fewer errors of classification. The data used consisted of two sets of observations from multivariate normal distributions. The first set of data were from two 3-variate normal populations with different mean vectors and common dispersion matrix. From each of the two populations 5000 samples were generated. 1000 samples out of the 5000 were used to create the trees. The remaining 4000 samples from each population were used to test the trees. The second set of data were from three 4-variate normal populations with different mean vectors and common dispersion matrix. A similar sampling and testing procedure as for the case of first set of data was employed. Computations were implemented using R statistical package. The results from the test showed that fuzzy classification trees allocated observations to the correct population with fewer errors than did crisp classification tree.en_US
dc.identifier.citationEuropean Scientific Journal June 2014 edition vol.10, No.18en_US
dc.identifier.issn18577881
dc.identifier.issn18577431
dc.identifier.urihttp://ir-library.ku.ac.ke/handle/123456789/12178
dc.language.isoenen_US
dc.publisherEuropean Scientific Instituteen_US
dc.subjectCrisp classification tree, Fuzzy classification tree, Gini index, Fuzzy decision points, Crisp decision pointsen_US
dc.titleComparison of crisp and fuzzy classification trees using gini index impurity measure on simulated dataen_US
dc.typeArticleen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
COMPARISON OF CRISP AND FUZZY.pdf
Size:
141.46 KB
Format:
Adobe Portable Document Format
Description:
Full text Article
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
RP-Department of Mathematics