Applying undistorted neural network sensitivity analysis in iris plant classification and construction productivity prediction

The present research focuses on the development and applications of a sensitivity analysis technique on multi-layer perceptron (MLP) neural networks (NN), which eliminates distortions on the sensitivity measures due to dissimilar input ranges with different units of measure for input features of both continuous and symbolic types in NN’s practical engineering applications. The effect of randomly splitting the dataset into training and testing sets on the stability of a MLP network’s sensitivity is also observed and discussed. The IRIS-UCI dataset and a real concreting productivity dataset serve as case studies to illustrate the validity of the undistorted sensitivity measure proposed. The results of the two case studies lead to the conclusion that the sensitivity measures accounting for the relevant input range for each input feature are more accurate and effective for revealing the relevance of each input feature and identifying less significant ones for potential feature reduction on the model. The MLP NN model obtained in such a way can give not only high prediction accuracy, but also valid sensitivity measures on its input features, and hence can be deployed as a predictive tool for supporting the decision process on new scenarios within the engineering problem domain.