提高分类性能的特征选择算法综述

K. Naidu, Aparna Dhenge, K. Wankhade
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引用次数: 13

摘要

随着计算机科学技术的飞速发展,如何快速查找到有用或需要的信息已成为困扰用户的一大难题。数据挖掘可以被视为人工智能的一个领域,它试图从存储在数据库中的大量数据中提取信息或模式。近年来,人们对特征选择进行了研究,试图找到有效的方法来选择相关的特征。特征选择通常涉及搜索和属性的组合,效用估计加上对特定学习方案的评估。集成系统的特征选择方法有很多种,遗传算法是最常用的方法之一。本文概述了特征选择算法,该算法利用进化计算的思想对特征空间进行搜索,以找到最优的特征子集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Feature Selection Algorithm for Improving the Performance of Classification: A Survey
With the rapid development of the Computer Science and Technology, It has become a major problem for the users that how to quickly find useful or needed information. Data mining can be seen as an area of artificial intelligence that seeks to extract information or patterns from large amounts of data stored in databases. Recent researches on feature selection have been conducted in an attempt to find efficient methods for selection of relevant features. Feature selection generally involves a combination of search and attributes utility estimation plus evaluation with respect to specific learning schemes. There are several methods to select features in ensembles systems and genetic algorithms (GA) are one of the most used methods. This paper gives overview of feature selection Algorithm which searches the feature space using the idea of evolutionary computation, in order to find the optimal feature subset.
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