Authors : K. Thangavel , A. Pethalakshmi and P. Jaganathan
Abstract: Database with missing values is a common phenomenon in data mining, statistical analysis, as well as in machine learning. Missing values in the database will affect the classification accuracy and effectiveness of classification rules. In this study, we have used four different methods such as Indiscernibility, Mean, Median and Mode for dealing with missing attribute values and proposed a Revised Quickreduct algorithm for dimensionality reduction. A comparative study is also performed with Revised and original Quickreduct algorithms based on the four different methods. The public domain datasets available in UCI machine learning repository with missing attribute values are used.
K. Thangavel , A. Pethalakshmi and P. Jaganathan , 2006. A Novel Reduct Algorithm for Dimensionality Reduction with Missing Values Based on Rough Set Theory. International Journal of Soft Computing, 1: 111-117.