摘要
为了获得决策系统中属性最小相对约简,将信息论角度定义的属性重要性度量作为启发式信息引入遗传算法,提出了一种启发式遗传算法.通过构造一个新的算子来引入启发式信息,使得选择的属性子集的分类能力不变.该算子体现了一种利用启发式信息的局部搜索技术,使得算法既保持整体优化特性,又具有较快的收敛速度.从理论上对算法作了分析,对新算子所能选择属性子集的分类能力不变作了证明.最后的实例表明,该算法能有效地对决策系统进行最大程度的约简.
Attribute reduction is an important process in data mining based on rough set.Regarding the significance of attributes defined from the viewpoint of information theory as heuristic information, and introducing the heuristic information into genetic algorithm, an effective heuristic genetic algorithm for minimizing relative reduction is proposed. A new operator is used for introducing the heuristic information so as to maintain the ability of classification of the attributes set. The operator is an embodiment of local research method using heuristic information. So the algorithm converges quickly and has global optimizing ability. The algorithm is also analyzed theoretically and the correctness and effectiveness of the algorithm are shown in the experiments.
出处
《西安交通大学学报》
EI
CAS
CSCD
北大核心
2002年第12期1286-1290,共5页
Journal of Xi'an Jiaotong University
基金
国家自然科学基金资助项目(69703011)
