Abstract
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.
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References
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)
Hart, P.E.: The condensed nearest neighbor rule. IEEE Transactions on Information Theory 14, 515–516 (1968)
Wilson, D.: Asymptotic properties of nearest neighbor rules using edited data. IEEE Transactions on Systems, Man, and Cybernetics 2, 408–421 (1972)
Jankowski, N., Grochowski, M.: Comparison of Instances Seletion Algorithms I. Algorithms Survey. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 598–603. Springer, Heidelberg (2004)
Kuncheva, L., Bezdek, J.C.: Presupervised and postsupervised prototype classifier design. IEEE Transactions on Neural Networks 10, 1142–1152 (1999)
Wilson, D.R., Martinez, D., Reduction, T.: techniques for instance-based learning algorithms. Machine Learning 38, 251–268 (2000)
Salvador, G., Derrac, J., Ramon, C.: Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study. IEEE Transactions on Pattern Analysis and Machine Intelligence 34, 417–435 (2012)
Kovahi, R., John, G.: Wrappers for Feature Subset Selection. AIJ special issue on relevance (May 1997)
Zhang, J., et al.: Intelligent selection of instances for prediction functions in lazy learning algorithms. Artifcial Intelligence Review 11, 175–191 (1997)
Tolvi, J.: Genetic algorithms for outlier detection and variable selection in linear regression models. Soft Computing 8, 527–533 (2004)
Guillen, A., et al.: Applying Mutual Information for Prototype or Instance Selection in Regression Problems. In: ESANN 2009 Proceedings (2009)
Tomek, I.: An experiment with the edited nearest-neighbor rule. IEEE Transactions on Systems, Man, and Cybernetics 6, 448–452 (1976)
Kordos, M., Blachnik, M., Strzempa, D.: Do We Need Whatever More Than k-NN? In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010, Part I. LNCS (LNAI), vol. 6113, pp. 414–421. Springer, Heidelberg (2010)
Merz, C., Murphy, P.: UCI repository of machine learning databases (1998-2012), http://www.ics.uci.edu/mlearn/MLRepository.html
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Kordos, M., Blachnik, M. (2012). Instance Selection with Neural Networks for Regression Problems. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33266-1_33
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DOI: https://doi.org/10.1007/978-3-642-33266-1_33
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