MLP networks for classification and prediction with rule extraction mechanism
暂无分享,去创建一个
This work describes the use of direct supervised multi layer perceptron network (MLP) with one hidden layer. Its weights are adjusted by the backpropagation algorithm. In an artificial neural network (ANN), the knowledge of the domain specialists is represented by the topology of the ANN and by the values of the weights used. Thus, it is considerably difficult to explain to a specialist of a domain, how an ANN achieved its outputs. In order to solve this problem, we utilize a rules extraction mechanism, from the trained network, of the kind IF/THEN to explain the results obtained by the network. It is worth noting that such rules are more acceptable by specialists, due to their resemblance to the human reasoning. In order to accomplish this task, a breast cancer database and another with minimum indexes from BOVESPA were adopted to assess the capacity for classification and prediction of the implemented model.
[1] George Cybenko,et al. Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..
[2] Kishan G. Mehrotra,et al. Forecasting the behavior of multivariate time series using neural networks , 1992, Neural Networks.
[3] Lutz Prechelt,et al. A Set of Neural Network Benchmark Problems and Benchmarking Rules , 1994 .
[4] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.