摘要
在数据挖掘和机器学习领域,缺失数据经常出现,本文从理论和实验两方面分析了常用的几种处理缺失数据的方法的优、缺点。
Missing data and inconsistent data has been a pervasive problem in data mining and machine learning. In this paper, we compare the performance of several popular imputation methods for imputing missing data in machine learning and statistics about prediction accuracy and classification accuracy in our experiments.
出处
《南宁师范高等专科学校学报》
2007年第3期148-150,共3页
Journal of Nanning Junior Teachers College
关键词
数据挖掘
缺失数据
机器学习
data mining
missing data
machine learning
