摘要
The motivation of data mining is how to extract effective information from huge data in very large database. However, some redundant and irrelevant attributes, which result in low performance and high computing complexity, are included in the very large database in general.So, Feature Subset Selection (FSS) becomes one important issue in the field of data mining. In this letter, an FSS model based on the filter approach is built, which uses the simulated annealing genetic algorithm. Experimental results show that convergence and stability of this algorithm are adequately achieved.
The motivation of data mining is how to extract effective information from huge data in very large database. However, some redundant and irrelevant attributes, which result in low performance and high computing complexity, are included in the very large database in general. So, Feature Subset Selection (FSS) becomes one important issue in the field of data mining. In this letter, an FSS model based on the filter approach is built, which uses the simulated annealing genetic algorithm. Experimental results show that convergence and stability of this algorithm are adequately achieved.
基金
Supported by the Project of the Science and Technology Plan of Chongqing City
