基于分割策略的特征选择算法

doi:10.11896／j.issn.1002-137X.2018.10.008

摘要/Abstract

摘要： 特征选择是粗糙集理论中最基本、最重要的研究内容之一。已有的大多数特征选择算法对小规模数据表较为有效。在信息时代,数据表的规模越来越大,传统的特征选择方法对于大规模数据表的计算效率非常低。因此,文中引入分割策略的思想,将大规模数据表分割成若干个较小规模的数据表,然后通过合并所得结果来解决原数据表的特征选择问题。在标准数据集上的实验结果表明了所提算法的有效性。

关键词: 粗糙集理论, 分割, 核表, 冗余表, 特征选择

Abstract: Feature selection is a key issue in rough sets.The theory of rough sets is an efficient tool for reducing redundancy.At pre-sent,there are many items and features in a large table,but few methods can gain better performance for big data table.The idea of segmentation was introduced in this paper.A big data table is divided into several small tables,and selection results are joined together to solve feature selection problem of the original table.To evaluate the performance of the proposed method,this paper applied it to the benchmark data sets.Experimental results illustrate that the proposed method is effective.

Key words: Core-table, Feature selection, Redundant-table, Rough set theory, Segmentation

中图分类号:

TP181

焦娜. 基于分割策略的特征选择算法[J]. 计算机科学, 2018, 45(10): 43-46. https://doi.org/10.11896／j.issn.1002-137X.2018.10.008

JIAO Na. Feature Selection Algorithm Based on Segmentation Strategy[J]. Computer Science, 2018, 45(10): 43-46. https://doi.org/10.11896／j.issn.1002-137X.2018.10.008

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