面向成组对象集的增量式属性约简算法

现实世界中数据集都是动态变化的，非增量式属性约简方法从头重新计算原始数据集，而且未考虑先前约简结果中的信息，将耗费大量的时间和空间。为此，讨论了动态数据环境下约简的不变性，提出了一种面向成组对象集的增量式属性约简算法，利用先前约简中信息来快速获取强传承性的约简，从而提高增量式学习算法效率。最后，将该算法与非增量式约简方法和面向单个对象的增量式约简方法在UCI数据集和人工数据集上进行了相关比较。实验结果表明，面向成组对象的增量式属性约简算法能够快速处理动态数据，具有较好的约简传承性。
Real-world datasets change in size dynamically. Non-incremental attribute reduction methods usually need to re-compute source data when obtaining a new reduction without considering the information in the existing reduction, which consumes a great deal of computational time and storage space. Therefore, in this paper, some reduction invariance properties for dynamic datasets are discussed. An incremental attribute reduction algorithm for group objects using the previous reduction is proposed to quickly update a reduction with high inheritance rate and thus improve the efficiency of incremental learning. Finally, the incremental approach proposed is compared with an existing incremental attribute reduction algorithm for a single object, the non-incremental attribute reduction algorithms on the UCI, and synthetic datasets. Experimental results show that this incremental attribute reduction algorithm for group objects can deal with dynamic data rapidly, as it has better inheritance of reduction