Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
DNA visual and analytic data mining
VIS '97 Proceedings of the 8th conference on Visualization '97
Information Visualization and Visual Data Mining
IEEE Transactions on Visualization and Computer Graphics
IV '04 Proceedings of the Information Visualisation, Eighth International Conference
IF-THEN rules in neural networks for classification
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-2 (CIMCA-IAWTIC'06) - Volume 02
The Colour Image Processing Handbook (Optoelectronics, Imaging and Sensing)
The Colour Image Processing Handbook (Optoelectronics, Imaging and Sensing)
Combined numerical and linguistic knowledge representation and its application to medical diagnosis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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In this paper, the circle segments (CS) technique is proposed as a data visualization tool for selecting and analysing the effects of the input features towards the target outputs in constructing neural network models. Specifically, the multi-layer perceptron (MLP) network is employed to tackle function approximation and pattern classification tasks, and CS is used to provide visualization of the correlations between the input features and the target outputs in these tasks. The effectiveness of the proposed approach is evaluated using two benchmark data sets, one for function approximation and another for pattern classification. Performance comparison with the response surface methodology (in function approximation) and with principal component analysis (in pattern classification) is conducted. The results indicate the usefulness of CS in examining the correlations between the input-output data samples, with improved performances. In addition, a real medical diagnosis task is used to evaluate the applicability of the approach. The outcomes, again, demonstrate improvements in accuracy, sensitivity, and specificity with the use of CS for feature selection, even with more than 50% of the input features eliminated.