计算机科学 ›› 2017, Vol. 44 ›› Issue (Z11): 129-132.doi: 10.11896/j.issn.1002-137X.2017.11A.026
王蓉,刘遵仁,纪俊
WANG Rong, LIU Zun-ren and JI Jun
摘要： 传统的ID3决策树算法存在属性选择困难、分类效率不高、抗噪性能不强、难以适应大规模数据集等问题。针对该情况,提出一种基于属性重要度及变精度粗糙集的决策树算法,在去除噪声数据的同时保证了决策树的规模不会太庞大。利用多个UCI标准数据集对该算法进行了验证,实验结果表明该算法在所得决策树的规模和分类精度上均优于ID3算法。
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