南昌工程学院学报2006，Vol.25Issue(2)：83-87,91,6.
Rough Set Assisted Meta-Learning Method to Select Learning Algorithms
Rough Set Assisted Meta-Learning Method to Select Learning Algorithms
Lisa Fan 1Min-xiao Lei1
作者信息
- 1. Department of Computer Science, University of Regina Regina, Saskatchewan S4S 0A2, Canada
- 折叠
摘要
Abstract
In this paper,we propose a Rough Set assisted Meta-Learning method on how to select the most-suited machine-learning algorithms with minimal effort for a new given dataset. A k-Nearest Neighbor (k-NN) algorithm is used to recognize the most similar datasets that have been performed by all of the candidate algorithms. By matching the most similar datasets we found,the corresponding performance of the candidate algorithms is used to generate recommendation to the user. The performance derives from a multi-criteria evaluation measure-ARR, which contains both accuracy and time. Furthermore, after applying Rough Set theory, we can find the redundant properties of the dataset. Thus,we can speed up the ranking process and increase the accuracy by using the reduct of the meta attributes.关键词
Meta-Learning/algorithm recommendation/Rough setsKey words
Meta-Learning/algorithm recommendation/Rough sets分类
数理科学引用本文复制引用
Lisa Fan,Min-xiao Lei..Rough Set Assisted Meta-Learning Method to Select Learning Algorithms[J].南昌工程学院学报,2006,25(2):83-87,91,6.
