Performance comparison of State-of-the-art Missing Value Imputation Algorithms on Some Bench mark Datasets

2013/07/22 by M. Naresh Kumar, Kumar, M. Naresh
Computer Science · Mathematics · #Algorithms and Data Compression #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Machine Learning and Data Classification #cs.LG #stat.ML

paper · pdf · doi:10.48550/arxiv.1307.5599

17 pages, 6 figures

arxiv created 2013/07/22 · openalex publication_date 2013/07/22 · arxiv updated 2013/07/23 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28

Abstract

Decision making from data involves identifying a set of attributes that contribute to effective decision making through computational intelligence. The presence of missing values greatly influences the selection of right set of attributes and this renders degradation in classification accuracies of the classifiers. As missing values are quite common in data collection phase during field experiments or clinical trails appropriate handling would improve the classifier performance. In this paper we present a review of recently developed missing value imputation algorithms and compare their performance on some bench mark datasets.

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