Stratified prototype selection based on a steady-state memetic algorithm: a study of scalability
نویسندگان
چکیده
Prototype selection (PS) is a suitable data reduction process for refining the training set of a datamining algorithm. Performing PS processes over existing datasets can sometimes be an inefficient task, especially as the size of the problem increases. However, in recent years some techniques have been developed to avoid the drawbacks that appeared due to the lack of scalability of the classical PS approaches. One of these techniques is known as stratification. In this study, we test the combination of stratification with a previously published steady-state memetic algorithm for PS in various problems, ranging from50,000 tomore than 1million instances. We perform a comparison with some well-known PS methods, and make a deep study of the effects of stratification in the behavior of the selected method, focused on its time complexity, accuracy and convergence capabilities. Furthermore, the trade-off between accuracy and efficiency of the proposed combination is analyzed, concluding that it is a very suitable option to perform PS tasks when the size of the problem exceeds the capabilities of the classical PS methods. J. Derrac (B) · F. Herrera Department of Computer Science and Artificial Intelligence, CITIC-UGR (Research Center on Information and Communications Technology), University of Granada, 18071 Granada, Spain e-mail: [email protected] F. Herrera e-mail: [email protected] S. García Department of Computer Science, University of Jaén, 23071 Jaén, Spain e-mail: [email protected]
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عنوان ژورنال:
- Memetic Computing
دوره 2 شماره
صفحات -
تاریخ انتشار 2010