期刊
KNOWLEDGE-BASED SYSTEMS
卷 239, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.knosys.2021.108079
关键词
Data science; Machine learning; Deep learning; Missing value imputation; Data discretization
资金
- Ministry of Science and Technology of Taiwan [MOST 110-2410-H-182-002]
- Chang Gung Memorial Hospital at Linkou [BMRPH13, CMRPG3J0732]
This study compares the performance of multilayer perceptron (MLP) and deep belief networks (DBN) in missing value imputation, with DBN performing the best. In addition, the research finds that when considering the discretization of continuous data, the choice of imputation algorithm is more important than the order in which the two steps are combined.
Often real-world datasets are incomplete and contain some missing attribute values. Furthermore, many data mining and machine learning techniques cannot directly handle incomplete datasets. Missing value imputation is the major solution for constructing a learning model to estimate specific values to replace the missing ones. Deep learning techniques have been employed for missing value imputation and demonstrated their superiority over many other well-known imputation methods. However, very few studies have attempted to assess the imputation performance of deep learning techniques for tabular or structured data with continuous values. Moreover, the effect on the imputation results when the continuous data need to be discretized has never been examined. In this paper, two supervised deep neural networks, i.e., multilayer perceptron (MLP) and deep belief networks (DBN), are compared for missing value imputation. Moreover, two differently ordered combinations of data discretization and imputation steps are examined. The results show that MLP and DBN significantly outperform the baseline imputation methods based on the mean, KNN, CART, and SVM, with DBN performing the best. On the other hand, when considering the discretization of continuous data, the order in which the two steps are combined is not the most important, but rather, the chosen imputation algorithm. That is, the final performance is much better when using DBN for imputation, regardless of whether discretization is performed in the first or second step, than the other imputation methods.(c) 2021 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据