Feature Selection using Compact Discernibility Matrix-based Approach in Dynamic Incomplete Decision System
Feature Selection using Compact Discernibility Matrix-based Approach in Dynamic Incomplete Decision System
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DOI：
10.6688/jise.2015.31.2.8
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发表时间：
2015-03
期刊：
影响因子：
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通讯作者：
Wenbin Qian;W. Shu;Yonghong Xie;Bingru Yang;Jun Yang
中科院分区：
文献类型：
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作者：
Wenbin Qian;W. Shu;Yonghong Xie;Bingru Yang;Jun Yang
根据系统是否随时间变化，决策系统可以分为两类：静态决策系统和动态决策系统。现有的特征选择工作大多是针对前者的，而针对后者的研究较少。就我们所知，当对象集在不完备决策系统中动态变化时，到目前为止还没有专门设计的特征选择方法来选择特征子集。对此，提出了一种基于紧致区分矩阵的特征选择算法。首先引入了紧致差别矩阵，不仅避免了计算耗时的下近似，而且比经典差别矩阵节省了更多的存储空间。然后，以下近似的变化为跳板，对紧致区分矩阵进行增量更新。在更新紧致区分矩阵的基础上，提出了一种高效的特征选择算法来计算新的特征子集，而不是从头开始保留区分矩阵来寻找新的特征子集。在不同数据集上的实验结果验证了该算法的有效性和有效性。
According to whether the systems vary over time, the decision systems can be divided into two categories: static decision systems and dynamic decision systems. Most existing feature selection work is done for the former, few work has been developed recently for the latter. To the best of our knowledge, when an object set varies dynamically in incomplete decision systems, no feature selection approach has been specially designed to select feature subset until now. In this regard, a feature selection algorithm based on compact discernibility matrix is developed. The compact discernibility matrix is firstly introduced, which not only avoids computing the time-consuming lower approximation, but also saves more storage space than classical discernibility matrix. Afterwards, we take the change of lower approximation as a springboard to incrementally update the compact discernibility matrix. On the basis of updated compact discernibility matrix, an efficient feature selection algorithm is provided to compute a new feature subset, instead of retaining the discernibility matrix from scratch to find a new feature subset. The efficiency and effectiveness of the proposed algorithm are demonstrated by the experimental results on different data sets.