Instance Selection with Neural Networks for Regression Problems
The paper presents algorithms for instance selection for regression problems based upon the CNN and ENN solutions known for classification tasks. A comparative experimental study is performed on several datasets using multilayer perceptrons and k-NN algorithms with different parameters and their various combinations as the method the selection is based on. Also various similarity thresholds are tested. The obtained results are evaluated taking into account the size of the resulting data set and the regression accuracy obtained with multilayer perceptron as the predictive model and the final recommendation regarding instance selection for regression tasks is presented.
Keywordsneural network instance selection regression
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- 1.Kordos, M., Blachnik, M., Wieczorek, T.: Temperature Prediction in Electric Arc Furnace with Neural Network Tree. In: Honkela, T. (ed.) ICANN 2011, Part II. LNCS, vol. 6792, pp. 71–78. Springer, Heidelberg (2011)Google Scholar
- 8.Kovahi, R., John, G.: Wrappers for Feature Subset Selection. AIJ special issue on relevance (May 1997)Google Scholar
- 11.Guillen, A., et al.: Applying Mutual Information for Prototype or Instance Selection in Regression Problems. In: ESANN 2009 Proceedings (2009)Google Scholar
- 14.Merz, C., Murphy, P.: UCI repository of machine learning databases (1998-2012), http://www.ics.uci.edu/mlearn/MLRepository.html