NDLI: Using the ADTree for feature reduction through knowledge discovery

Content Provider	IEEE Xplore Digital Library
Author	Hong Kuan Sok Chowdhury, M.S. Ooi, M.P.-L. Demidenko, S.
Copyright Year	2013
Description	Author affiliation: Sch. of Eng., Monash Univ., Sunway, Malaysia (Hong Kuan Sok; Ooi, M.P.-L.) \|\| Centre of Technol., RMIT Int. Univ., Vietnam (Chowdhury, M.S.; Demidenko, S.)
Abstract	There is a chicken-and-egg problem in classification whereby a good classifier is required to test the efficacy of the features, yet a good feature set is required to generate a good classifier. When the salient features are unknown, an extremely large set of features is used to train the classifier in hopes of obtaining accurate classification results. This research proposes the use of a special class of decision tree called the alternating decision tree or ADTree to answer two questions in knowledge discovery in order to effectively select a salient feature set: When using a particular feature extraction algorithm, which of the features is able to distinguish between the different classes? And how do they work?
Sponsorship	IEEE Instrum. Meas. Soc.
Starting Page	1040
Ending Page	1044
File Size	111074
Page Count	5
File Format	PDF
ISBN	9781467346214
ISSN	10915281
e-ISBN	9781467346238
DOI	10.1109/I2MTC.2013.6555573
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Feature extraction Boosting Decision trees Classification algorithms Training Accuracy Knowledge Discovery ADTree SVM HOG
Content Type	Text
Resource Type	Article
Subject	Instrumentation Electrical and Electronic Engineering

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