NDLI: Evaluating skin condition using a new decision tree induction algorithm

Content Provider	IEEE Xplore Digital Library
Author	Ming Dong Kothari, R. Visscher, M. Hoath, S.B.
Copyright Year	2001
Description	Author affiliation: Artificial Neural Syst. Lab., Cincinnati Univ., OH, USA (Ming Dong)
Abstract	Decision tree induction is well suited for applications requiring simple, explicit and intuitive classification structure. Due to the deteriorating generalization performance with increasing size and depth of the tree, construction of decision trees of small size and depth is a fundamental to widespread realization of the many benefits of decision tree based classification. In this paper we present a decision tree induction method based on a novel classifiability measure. The proposed algorithm makes a decision at a node based on the number of correctly classified instances at the node as well as the classifiability of the incorrectly classified instances. We demonstrate the efficacy of the proposed algorithm using a biomedical dataset in which optical images of human infant skin, coupled with localized noninvasive biophysical measurement of epidermal skin barrier properties are used to evaluate the health of the skin.
Sponsorship	Int. Neural Network Soc.
Starting Page	2456
Ending Page	2460
File Size	442125
Page Count	5
File Format	PDF
ISBN	0780370449
ISSN	10987576
DOI	10.1109/IJCNN.2001.938752
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-07-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Skin Decision trees Classification tree analysis Biomedical measurements Statistics Testing Laboratories Computer science Greedy algorithms Biomedical optical imaging
Content Type	Text
Resource Type	Article

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