Abstract
In order to deal with big data, techniques for prototype selection have been applied for reducing the computational resources that are necessary to apply data mining approaches. However, most of the proposed approaches for prototype selection have a high time complexity and, due to this, they cannot be applied for dealing with big data. In this paper, we propose an efficient approach for prototype selection. It adopts the notion of spatial partition for efficiently dividing the dataset in sets of similar instances. In a second step, the algorithm extracts a prototype of each of the densest spatial partitions that were previously identified. The approach was evaluated on 15 well-known datasets used in a classification task, and its performance was compared to those of 6 state-of-the-art algorithms, considering two measures: accuracy and reduction. All the obtained results show that, in general, the proposed approach provides a good trade-off between accuracy and reduction, with a significantly lower running time, when compared with other approaches.
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Notes
- 1.
The source code of the algorithm is available in https://www.researchgate.net/publication/323701200_PSDSP_algorithm.
- 2.
Notice that we are assuming in this paper that the set of natural numbers \((\mathbb {N}\)) is the set of non-negative integers and, due to this, it includes zero. When we are referring to the set of natural numbers excluding zero, we use \(\mathbb {N}^{*}\).
- 3.
All algorithms were implemented by the authors.
- 4.
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Carbonera, J.L., Abel, M. (2018). An Efficient Prototype Selection Algorithm Based on Dense Spatial Partitions. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10842. Springer, Cham. https://doi.org/10.1007/978-3-319-91262-2_26
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DOI: https://doi.org/10.1007/978-3-319-91262-2_26
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