Dealing with Missing Values in Data

By J. Kaiser. 2014

143 citations recorded by the source corpus.

Many existing industrial and research data sets contain missing values due to various reasons, such as manual data entry procedures, equipment errors and incorrect measurements. Problems associated with missing values are loss of efficiency, complications in handling and analyzing the data and bias resulting from differences between missing and complete data. The important factor for selection of approach to missing values is missing data mechanism. There are various strategies for dealing with missing values. Some analytical methods have their own approach to handle missing values. Data set reduction is another option. Finally missing values problem can be handled by missing values imputation. This paper presents simple methods for missing values imputation like using most common value, mean or median, closest fit approach and methods based on data mining algorithms like k-nearest neighbor, neural networks and association rules, discusses their usability and presents issues with their applicability on examples.

Subfields: Computational Theory and Mathematics, Information Systems, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/db20c7b048bf1f39a7e74d7aeb0d127221bae54e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Xiaojie Yuan
2. Kwetishe Joro Danjuma
3. Xiangrui Cai
4. Yonghong Luo
5. Jun Xu
6. Y. Zhang

In Information Systems

1. Xiaojie Yuan
2. Kwetishe Joro Danjuma
3. Xiangrui Cai
4. Yonghong Luo
5. Jun Xu
6. Y. Zhang
7. Hang Li
8. Tie-Yan Liu

In Statistics and Probability

1. Xiaojie Yuan
2. Kwetishe Joro Danjuma
3. Xiangrui Cai
4. Yonghong Luo
5. Jun Xu
6. Y. Zhang

Citations in the corpus

• A Comparison of Multiple Imputation Methods for Data with Missing Values (2017, unknown): “Mean or Median from the observed data is being used to replace the missing values and it generates biased parameter estimates because missing values are substituted with measure of central tendency of the distribution 16,18,20,21 .”
• Multivariate Time Series Imputation with Generative Adversarial Networks (2018, methodology): “The very first one is case deletion methods [26, 43].”
• Performance Evaluation of Machine Learning Algorithms in Post-operative Life Expectancy in the Lung Cancer Patients (2015, methodology): “…to handle missing values imputation includes; a) data reduction and elimination of all missing values; b) use of most common values, mean or median; and c) closest fit approach and methods based on machine learning algorithms such as k-nearest neighbor, neural networks and association rules [25].”