Abstract
With the increasing size of the data, reducing dataset to reduce computational complexity has become an important task. Instance selection is one of the common preprocessing processes in data mining, which can delete the redundant instances and noisy points from dataset. In the past, various instance selection algorithms have been proposed. Most of them are effective for selecting convex instances, but fail to achieve good performances when dealing with concave instances. This paper proposes a hybrid instance selection algorithm based on convex hull and nearest neighbor information. Firstly, he proposed algorithm identifies the nearest enemy for each data point, and then divides the dataset into several subsets by grouping the points, which have the same nearest enemy, into one subset. Finally, the convex hull algorithm is executed on each subset to select the convex hull points. Our algorithm is evaluated on 14 datasets and compared with some traditional instance selection algorithms. The experimental results show that the proposed algorithm performs better than other traditional algorithms.
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Wang, S., Tian, J. (2020). A Hybrid Instance Selection Method Based on Convex Hull and Nearest Neighbor. In: Chien, CF., Qi, E., Dou, R. (eds) IE&EM 2019. Springer, Singapore. https://doi.org/10.1007/978-981-15-4530-6_1
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DOI: https://doi.org/10.1007/978-981-15-4530-6_1
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