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A convex hull-based data selection method for data driven models
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DOI：10.1016/j.asoc.2016.06.014.png)
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摘要
En 中文
The accuracy of classification and regression tasks based on data driven models, such as Neural Networks or Support Vector Machines, relies to a good extent on selecting proper data for designing these models, covering the whole input range in which they will be employed. The convex hull algorithm can be applied as a method for data selection; however the use of conventional implementations of this method in high dimensions, due to its high complexity, is not feasible. In this paper, we propose a randomized approximation convex hull algorithm which can be used for high dimensions in an acceptable execution time, and with low memory requirements. Simulation results show that data selection by the proposed algorithm (coined as ApproxHull) can improve the performance of classification and regression models, in comparison with random data selection. (C) 2016 Elsevier B.V. All rights reserved.
Keyword:
Convex hull
Data selection problem
Classification
Regression
Neural networks
Support vector machines
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期刊
IF:
6.6
论文数:
1.4W
被引数:
5.9W
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分享收藏Large data sets classification using convex-concave hull and support vector machine.png)
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