Abstract
Ensemble learning is based on the divided-and-conquer principles but, after dividing, we would need to combine the partial results in some way to reach a final decision. Therefore, a crucial point when designing an ensemble method is to choose an appropriate method for combining the different weak outputs. There are several methods in the literature to solve this issue, and they are grouped according to whether the outputs are classification predictions, subsets of features or rankings of features. In this chapter we will describe methods falling in all these categories, so that the interesting readers can make an informed choice according to their needs trying to design the best ensemble possible.
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References
Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley, New Jersey (2004)
Peteiro-Barral, D., Guijarro-Berdiñas, B.: A survey of methods for distributed machine learning. Prog. Artif. Intell. 2(1), 1–11 (2013)
Álvarez-Estévez, D., Sánchez-Maroño, N., Alonso-Betanzos, A., Moret-Bonillo, V.: A survey of methods for distributed machine learning. Expert Syst. Appl. 38(6), 7746–7754 (2011)
Aguilar-Ruiz, J.S., Azuaje, F., Riquelme, J.C.: Data mining approaches to diffuse large B-Cell lymphoma gene expression data interpretation. Lecture Notes in Computer Science, pp. 279–288. Springer, Berlin (2004)
Bolón-Canedo, V., Sánchez-Maroño, N., Alonso-Betanzos, A.: Distributed feature selection: an application to microarray data classification. Appl. Soft Comput. 30, 136–150 (2015)
Bolón-Canedo, V., Sánchez-Maroño, N., Cerviño-Rabuñal, J.: Toward parallel feature selection from vertically partitioned data. In: Proceedings of European Symposium on Artificial Neural Networks, ESANN, pp. 395–400 (2014)
Basu, M., Ho, T.K.: Data Complexity in Pattern Recognition. Springer, Berlin (2006)
Morán-Fernández, L., Bolón-Canedo, V., Alonso-Betanzos, A.: Centralized versus distributed feature selection methods based on data complexity measures. Knowl. Based Syst. 117, 27–45 (2017)
Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. 20(4), 422–446 (2002)
Arrow, K.J.: Social Choice and Individual Values. Wiley, New Jersey (1951)
Bolón-Canedo, V., Sechidis, K., Sánchez-Maroño, N., Alonso-Betanzos, A., Brown, G.: Exploring the consequences of distributed feature selection in DNA microarray data. In: Proceedings of international joint conference on neural networks, IJCNN, pp. 1665–1672 (2017)
Kolde, R., Laur, S., Adler, P., Vilo, J.: Robust rank aggregation for gene list integration and meta-analysis. Bioinformatics 28(4), 573–580 (2012)
Stuart, J., Segal, E., Koller, D., Kim, S.K.: A gene-coexpression network for global discovery of conserved genetic modules. Science 302(5643), 249–255 (2003)
Aerts, S., Lambrechts, D., Maity, S., Van Loo, P., Coessens, B., Tranchevent, L.C., De Moor, B., Marynen, P., Hassan, B., Carmeliet, P., et al.: Gene prioritization through genomic data fusion. Nat. Biotechnol. 24(5), 537–544 (2006)
Joachims, T.: Optimizing search engines using clickthrough data. In: Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining, pp. 133–142 (2002)
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Bolón-Canedo, V., Alonso-Betanzos, A. (2018). Combination of Outputs. In: Recent Advances in Ensembles for Feature Selection. Intelligent Systems Reference Library, vol 147. Springer, Cham. https://doi.org/10.1007/978-3-319-90080-3_5
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DOI: https://doi.org/10.1007/978-3-319-90080-3_5
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