The paper introduces author's job on realization of continuous attribute discretization based on languagefield theory that Prof. Yang put forward. It applies a new algorithm of seeking border values and its increm...The paper introduces author's job on realization of continuous attribute discretization based on languagefield theory that Prof. Yang put forward. It applies a new algorithm of seeking border values and its incremental onerather than seeking boundary ones that is a difficulty. The theory of the algorithm is self-contained, and its realiza-tion is simple. And the paper introduces simply four thoughts about defining language values and then discretizing fornon-numerical value that author already realized in KDD * .展开更多
数据属性离散化是作战仿真数据预处理的重要组成部分,也是作战仿真数据研究的重点和难点。论述了进行数据属性离散化的必要性,提出一种基于改进属性重要度和信息熵(Discretization by Improved Attribute Significance and Information ...数据属性离散化是作战仿真数据预处理的重要组成部分,也是作战仿真数据研究的重点和难点。论述了进行数据属性离散化的必要性,提出一种基于改进属性重要度和信息熵(Discretization by Improved Attribute Significance and Information Entropy,DIAFIE)的作战仿真数据属性离散化算法。算法定义了属性重要度并以此为聚类判断依据将数据值域划分为多个离散区间,然后根据信息熵优化合并相邻区间以保证离散化结果的精度。实验证明上述算法能有效处理作战仿真数据属性离散化问题,具有产生断点少、分类精度高的优点。展开更多
文摘The paper introduces author's job on realization of continuous attribute discretization based on languagefield theory that Prof. Yang put forward. It applies a new algorithm of seeking border values and its incremental onerather than seeking boundary ones that is a difficulty. The theory of the algorithm is self-contained, and its realiza-tion is simple. And the paper introduces simply four thoughts about defining language values and then discretizing fornon-numerical value that author already realized in KDD * .
文摘数据属性离散化是作战仿真数据预处理的重要组成部分,也是作战仿真数据研究的重点和难点。论述了进行数据属性离散化的必要性,提出一种基于改进属性重要度和信息熵(Discretization by Improved Attribute Significance and Information Entropy,DIAFIE)的作战仿真数据属性离散化算法。算法定义了属性重要度并以此为聚类判断依据将数据值域划分为多个离散区间,然后根据信息熵优化合并相邻区间以保证离散化结果的精度。实验证明上述算法能有效处理作战仿真数据属性离散化问题,具有产生断点少、分类精度高的优点。