NDLI: Induction of compact neural network trees through centroid based dimensionality reduction

Content Provider	IEEE Xplore Digital Library
Author	Hayashi, H. Qiangfu Zhao
Copyright Year	2009
Description	Author affiliation: Department of Computer and Information Systems, The University of Aizu, Aizuwakamatsu, Japan (Hayashi, H.; Qiangfu Zhao)
Abstract	Neural network tree (NNTree) is a hybrid model for machine learning. Compared with single model fully connected neural networks, NNTrees are more suitable for structural learning, and faster for decision making. To increase the realizability of the NNTrees, we have tried to induce more compact NNTrees through dimensionality reduction. So far, we have used principal component analysis (PCA) and linear discriminant analysis (LDA) for dimensionality reduction, and confirmed that in most cases the LDA based approach can result in very compact NNTrees without degrading the performance. One drawback in using the LDA based approach is that the cost for finding the transformation matrix can be very high for large databases. To solve this problem, in this paper we investigate the efficiency and efficacy of two centroid based approaches for NNTree induction. One is to map each datum directly to the class centroids; and the other is to find the least square error approximation of each datum using the centroids. Experimental results show that both approaches, although simple, are comparable to the LDA based approach in most cases.
Starting Page	948
Ending Page	953
File Size	178109
Page Count	6
File Format	PDF
ISBN	9781424427932
ISSN	1062922X
DOI	10.1109/ICSMC.2009.5346091
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Biological neural networks Neurons Linear discriminant analysis Decision trees Decision making Principal component analysis Costs Least squares approximation Cybernetics multivariate decision tree Machine learning pattern recognition decision tree neural network
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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