Abstract
We present an Information Selection and Data Compression RapidMiner Library, which contains several known instance selection algorithms and several algorithms developed by us for classification and regression tasks. We present the motivation for creating the library and the need for developing new instance selection algorithms or extending the existing ones. We discuss how the library works and how to use it.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Asuncion, A., Newman, D.: UCI machine learning repository. http://www.ics.uci.edu/~mlearn/MLRepository.html (2007)
Aha, D., Kibler, D., Albert, M.: Instance-based learning algorithms. Mach. Learn. 6, 37–66 (1991)
Blachnik, M.: Information selection and prototype-based rules. http://www.prules.org
Blachnik, M.: Ensembles of instance selection methods based on feature subset. IEEE Procedia Comput. Sci. 35, 388–396 (2014)
Blachnik, M.: Reducing time complexity of svm model by lvq data compression. Submitted to ICAISC (2015)
Blachnik, M., Kordos, M.: Bagging of instance selection algorithms. LNAI 8468, 40–51 (2014)
Cameron-Jones, R.: Instance selection by encoding length heuristic with random mutation hill climbing. In: Proceedings of the Eighth Australian Joint Conference on Artificial Intelligence, pp. 99–106 (1995)
Grochowski, M., Jankowski, N.: Comparison of instance selection algorithms ii. Results and Comments. LNCS 3070, 580–585 (2004)
Garcia, S., Derrac, J., Cano, J., Herrera, F.: Prototype selection for nearest neighbor classification: taxonomy and empirical study. IEEE Trans. Pattern Anal. Mach. Intell. 34(3), 417–435. http://dx.doi.org/10.1109/TPAMI.2011.142 (2012)
Hart, P.: The condensed nearest neighbor rule. IEEE Trans. Inf. Theory 16, 515–516 (1968)
Kohonen, T.: Learning vector quantization. Self-organizing Maps, pp. 203–217. Springer, New York (1997)
Kordos, M., Rusiecki, A.: Improving mlp neural network performance by noise reduction. Lect. Notes Comput. Sci. 8273, 140–151 (2013)
Kordos, M., Blachnik, M., Bialka, S.: Instance selection in logical rule extraction for regression problems. LNAI 7895, 167–175 (2013)
Pedrycz, W.: Conditional fuzzy c-means. Pattern Recognit. Lett. 17, 625–632 (1996)
Skalak, D.: Prototype and feature selection by sampling and random mutation hill climbing algorithms. In: Proceedings of the 11th International Conference on Machine Learning, pp. 293–301. Citeseer (1994)
Sánchez, J., Pla, F., Ferri, F.: Prototype selection for the nearest neighbour rule through proximity graphs. Pattern Recognit. Lett. 18(6), 507–513 (1997)
Tomek, I.: An experiment with the edited nearest-neighbor rule. IEEE Trans. Syst., Man, Cybern. 6, 448–452 (1976)
Wilson, D.: Assymptotic properties of nearest neighbour rules using edited data. IEEE Trans. Syst., Man, Cybern. SMC-2, 408–421 (1972)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Blachnik, M., Kordos, M. (2016). Information Selection and Data Compression RapidMiner Library. In: Ryżko, D., Gawrysiak, P., Kryszkiewicz, M., Rybiński, H. (eds) Machine Intelligence and Big Data in Industry. Studies in Big Data, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-319-30315-4_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-30315-4_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-30314-7
Online ISBN: 978-3-319-30315-4
eBook Packages: EngineeringEngineering (R0)