【摘要】 连续属性的离散化是数据预处理的重要工作。论文分析了基于熵的离散化方法的不足,从估计训练样本的概率分布的角度出发,提出基于样本分布与熵相结合的处理数值型属性的方法。基于UCI数据的实验结果表明,该方法不仅具有比较好的判决精度,而且具有更快的计算速度。更多还原

【Abstract】 Discretization of numeric attribute is an important role of data preprocessing.A heavy analysis about discretization method based on entropy is given.By the method of estimating the probability distribution of training examples,a new and simple method of dealing with numeric attribute based on example distribution and entropy is turned out.Experimental results of UCI data sets show that the proposed method has good performance on accuracy issue and the computational speed is heightened greatly.更多还原