Rule Extraction using Artificial Neural Networks

Details Rule Extraction using Artificial Neural Networks Rule Extraction using Artificial Neural Networks

2010-09-25

arxiv.org/abs/1009.4984v1

Proc. International Conference on Information and Communication Technology in Management (ICTM 2005), Multimedia University, M

Computer Science

Neural and Evolutionary Computing

14 Pages, International Conference

Scientific paper

Artificial neural networks have been successfully applied to a variety of business application problems involving classification and regression. Although backpropagation neural networks generally predict better than decision trees do for pattern classification problems, they are often regarded as black boxes, i.e., their predictions are not as interpretable as those of decision trees. In many applications, it is desirable to extract knowledge from trained neural networks so that the users can gain a better understanding of the solution. This paper presents an efficient algorithm to extract rules from artificial neural networks. We use two-phase training algorithm for backpropagation learning. In the first phase, the number of hidden nodes of the network is determined automatically in a constructive fashion by adding nodes one after another based on the performance of the network on training data. In the second phase, the number of relevant input units of the network is determined using pruning algorithm. The pruning process attempts to eliminate as many connections as possible from the network. Relevant and irrelevant attributes of the data are distinguished during the training process. Those that are relevant will be kept and others will be automatically discarded. From the simplified networks having small number of connections and nodes we may easily able to extract symbolic rules using the proposed algorithm. Extensive experimental results on several benchmarks problems in neural networks demonstrate the effectiveness of the proposed approach with good generalization ability.

Affiliated with

Also associated with

No associations

Rating

Rule Extraction using Artificial Neural Networks does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Rule Extraction using Artificial Neural Networks, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Rule Extraction using Artificial Neural Networks will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-695846