【作者】 王宏威； 李国和； 李雪； 吴卫江； 李洪奇；

【Author】 WANG Hong-wei~(1,2),LI Guo-he~1,LI Xue~3,WU Wei-jiang~1,LI Hong-qi~1 (1.State Key Laboratory of Petroleum Resource and Prospecting,College of Geophysics and Information Engineering, China University of Petroleum,Beijing 102249,China; 2.College of Information Science and Technology,Bohai University,Jinzhou 121013,China; 3.School of Information Technology and Electrical Engineering,the University of Queensland, Brisbane,4072,Australia)

【机构】 中国石油大学(北京) 地球物理与信息工程学院 油气资源与探测国家重点实验室； 渤海大学 信息科学与技术学院； School of Information Technology and Electrical Engineering,the University of Queensland；

【摘要】 根据对象在特征空间中的分布,划分连续特征空间为类别单一、边界清晰的多个子空间;把各个子空间分别投影到所有特征上,获取所有不同类别子空间对当前子空间特征分类能力的评估;通过构造分类能力评估矩阵,实现特征分类能力的特征排序。以特征排序为依据,选取特征子集,实现特征选取。实验结果表明,这种特征选取具有有效性和高效性。更多还原

【Abstract】 In terms of the distribution of objects and their classification labels,the continuous feature space was partitioned into a variety of subspaces,each one with clear edge and unique classification label.After the projection of all the subspaces to each feature,the quality of each feature was estimated for a subspace opposite to all the other subspaces with different classification labels by means of statistical significance.Through construction of a matrix by all the estimate qualities of all features of all the subspaces,all the features was ranked from the highest classifying power to the lowset on the matrix for the feature space.According to the ranked-feature set,the feature selection was completed.The experimental results illustrate that the feature selection is efficient and effective.更多还原