DNA visual and analytic data mining
VIS '97 Proceedings of the 8th conference on Visualization '97
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
Correlation-based Feature Selection Strategy in Neural Classification
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 01
Expert Systems with Applications: An International Journal
Combined numerical and linguistic knowledge representation and its application to medical diagnosis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Hi-index | 0.00 |
One of the issues associated with pattern classification using data-based machine learning systems is the "curse of dimensionality". In this paper, the circle-segments method is proposed as a feature selection method to identify important input features before the entire data set is provided for learning with machine learning systems. Specifically, four machine learning systems are deployed for classification, viz.Multilayer Perceptron (MLP), Support Vector Machine (SVM), Fuzzy ARTMAP (FAM), and k-Nearest Neighbour(kNN). The integration between the circle-segments method and the machine learning systems has been applied to two case studies comprising one benchmark and one real data sets. Overall, the results after feature selection using the circle-segments method demonstrate improvements in performance even with more than 50% of the input features eliminated from the original data sets.