Welcome to the InfoSci Platform
Missing Data Estimation Using Rough Sets

Missing Data Estimation Using Rough Sets

Tshilidzi Marwala
Copyright: © 2009 |Pages: 23
ISBN13: 9781605663364|ISBN10: 1605663360|ISBN13 Softcover: 9781616925604|EISBN13: 9781605663371
DOI: 10.4018/978-1-60566-336-4.ch005
Cite Chapter Cite Chapter

MLA

Tshilidzi Marwala. "Missing Data Estimation Using Rough Sets." Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques, IGI Global, 2009, pp.94-116. https://doi.org/10.4018/978-1-60566-336-4.ch005

APA

T. Marwala (2009). Missing Data Estimation Using Rough Sets. IGI Global. https://doi.org/10.4018/978-1-60566-336-4.ch005

Chicago

Tshilidzi Marwala. "Missing Data Estimation Using Rough Sets." In Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-336-4.ch005

Export Reference

Mendeley
Favorite

Abstract

A number of techniques for handling missing data have been presented and implemented. Most of these proposed techniques are unnecessarily complex and, therefore, difficult to use. This chapter investigates a hot-deck data imputation method, based on rough set computations. In this chapter, characteristic relations are introduced that describe incompletely specified decision tables and then these are used for missing data estimation. It has been shown that the basic rough set idea of lower and upper approximations for incompletely specified decision tables may be defined in a variety of different ways. Empirical results obtained using real data are given and they provide a valuable insight into the problem of missing data. Missing data are predicted with an accuracy of up to 99%.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.