In this paper, we introduce an algorithm which is using the importance of attribute with heuristics for feature selection.
本文提出一种基于属性重要度的启发式特征选取算法。
The former algorithm makes improvements from three aspects: discretization, reducing dimension, attribute selection, which effectively solves the conflict between efficiency and prediction precision.
前者从数据的离散化，降维，和属性选择方面有效的解决了处理大规模高维数据库时的效率与精度之间的矛盾。
The former algorithm makes improvements from three aspects: discretization, reducing dimension, attribute selection, which effectively solves the conflict between efficiency and prediction precision.
前者从数据的离散化，降维，和属性选择方面有效的解决了处理大规模高维数据库时的效率与精度之间的矛盾。
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