期刊
NEUROCOMPUTING
卷 118, 期 -, 页码 65-78出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2013.02.016
关键词
Locally linear reconstruction (LLR); Missing value imputation; Supervised learning; Classification; Regression
资金
- Basic Science Research Program through the National Research Foundation of Korea (NRF)
- Ministry of Education, Science, and Technology (MEST) [2011-0021893]
- National Research Foundation of Korea [2011-0021893] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Most learning algorithms generally assume that data is complete so each attribute of all instances is filled with a valid value. However, missing values are very common in real datasets for various reasons. In this paper, we propose a new single imputation method based on locally linear reconstruction (LLR) that improves the prediction performance of supervised learning (classification & regression) with missing values. First, we investigate how missing values degrade the prediction performance with various missing ratios. Next, we compare the proposed missing value imputation method (LLR) with six well-known single imputation methods for five different learning algorithms based on 13 classification and nine regression datasets. The experimental results showed that (1) all imputation methods helped to improve the prediction accuracy, although some were very simple; (2) the proposed LLR imputation method enhanced the modeling performance more than all other imputation methods, irrespective of the learning algorithms and the missing ratios; and (3) LLR was outstanding when the missing ratio was relatively high and its prediction accuracy was similar to that of the complete dataset. (C) 2013 Elsevier B.V. All rights reserved.
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