This paper presents FeaSANNT, an evolutionary procedure for feature selection and weight training for neural network classifiers. FeaSANNT exploits the global nature of evolutionary search to avoid sub-optimal peaks of performance. FeaSANNT was used to train a multi-layer perceptron classifier on seven benchmark problems. FeaSANNT attained accurate and consistent learning results, and significantly reduced the number of data attributes compared to four state-of-the-art standard filter and wrapper feature selection methods. Thanks to the robustness of evolutionary search, FeaSANNT did not require time-consuming re-tuning of the learning parameters for each test problem.
|IEEE International Conference on Industrial Informatics
|IEEE International Conference on Industrial Informatics (INDIN)
|1/01/09 → …