决策表最优特征子集的选择——基于粗集理论的启发式算法

Optimal Feature Subset Selection of Decision Tables

特征子集选择问题是机器学习的重要问题 .而最优特征子集的选择是NP困难问题 ,因此需要启发式搜索指导求解 .基于粗集理论 ,本文提出了一种新的决策表最优特征子集选择的启发式算法 .和以往的方法相比 ,这种算法简单实用 。

The feature subset selection is an important problem in machine learning, but the optimal feature subset selection is proves to be a NP hard one. Based on rough sets, a new heuristic algorithm is presented to solve the difficulty. To decision tables where the number of features reduces greatly after reduction, the algorithm is illustrated to be effective. Especially, it can give almost all the optimal solutions.