NDLI: A comparative study on GA based and BP based induction of neural network trees

Content Provider	IEEE Xplore Digital Library
Author	Hayashi, H. Qiangfu Zhao
Copyright Year	2005
Description	Author affiliation: Aizu Univ., Aizuwakamatsu, Japan (Hayashi, H.; Qiangfu Zhao)
Abstract	Neural network tree (NNTree) is a special multivariate decision tree (DT) with each nonterminal node containing an expert neural network (ENN). Generally speaking, NNTrees can outperform standard DTs because the ENNs can extract more complex features. However, induction of multivariate DTs is very difficult. Even if each nonterminal node contains a simple oblique hyperplane, the induction problem can be NP-complete. To solve this problem, we have introduced an evolutionary algorithm that follows the same recursive procedure for inducing a standard DT, and designs an ENN for each nonterminal node using GA (genetic algorithm). This algorithm, however, is very time consuming and cannot be used easily. In this paper, we propose two new methods. One is to evolve the whole tree instead of evolving the ENNs recursively. Another is to define the group labels for the examples assigned to each nonterminal node using a heuristic method, and design the ENNs with the back propagation (BP) algorithm. Experimental results with 10 public databases show that the BP based algorithm is much more efficient than GA based algorithms.
Starting Page	822
Ending Page	826
File Size	237041
Page Count	5
File Format	PDF
ISBN	0780392981
DOI	10.1109/ICSMC.2005.1571248
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Decision trees Machine learning algorithms Testing Evolutionary computation Algorithm design and analysis Machine learning Electronic mail Feature extraction Genetic algorithms decision trees pattern recognition neural networks neural network trees
Content Type	Text
Resource Type	Article

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