【摘 要】
：
属性选择是机器学习与模式识别中进行数据预处理的一个重要方法,特别是针对一些高维的数据集,其计算复杂度较高,对数据挖掘算法的性能影响较大.因此,文章在连续型萤火虫算法(
【机 构】
：
合肥工业大学管理学院,过程优化与智能决策教育部重点实验室,安徽省气象科学研究所安徽省大气科学与卫星遥感重点实验室,
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属性选择是机器学习与模式识别中进行数据预处理的一个重要方法,特别是针对一些高维的数据集,其计算复杂度较高,对数据挖掘算法的性能影响较大.因此,文章在连续型萤火虫算法(GSO)基础上对萤火虫进行二进制编码,并结合修正后的sigmoid函数,提出一种基于二进制萤火虫算法的属性选择方法.该方法以数据集分形维数作为属性子集的评价准则,以二进制萤火虫算法作为搜索策略,通过对标准数据集UCI进行一系列实验,实验结果表明了该方法的有效性与可行性.
Attribute selection is an important method for data pre-processing in machine learning and pattern recognition, especially for some high-dimensional datasets, which has high computational complexity and great impact on the performance of data mining algorithms.Therefore, Based on the firefly algorithm (GSO), and based on the modified sigmoid function, an attribute selection method based on the binary firefly algorithm is proposed.This method takes the data set fractal dimension as the evaluation criterion of the attribute subset, Using binary firefly algorithm as a search strategy, a series of experiments on the standard data set UCI are carried out. The experimental results show the effectiveness and feasibility of the proposed method.
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