A weight analysis-based wrapper approach to neural nets feature subset selection

This paper presents a novel attribute selection approach for backprop neural networks. Previously, an attribute selection technique known as the wrapper model was shown effective for decision tree induction. However, it is prohibitively expensive when applied to real-world neural net training characterized by large volumes of data and many attribute choices. Our approach incorporates a weight analysis based heuristic called ANNIGMA to direct the search in the wrapper model and allows effective attribute selection feasible for neural net applications. Experimental results on standard data sets show that this approach can efficiently reduce the number of inputs while maintaining or even improving the accuracy. We also report two successful applications of our approach in the helicopter maintenance applications.