A new imputation method for incomplete binary data

Authors:
Munevver Mine Subasi;Ersoy Subasi;Martin Anthony;Peter L. Hammer
Affiliations:
Department of Mathematical Sciences, Florida Institute of Technology, 150 W. University Blvd., Melbourne, FL 32901, USA;RUTCOR, Rutgers Center for Operations Research, 640 Bartholomew Road, Piscataway, NJ 08854, USA;Department of Mathematics, London School of Economics and Political Sciences, Houghton Street, London WC2A 2AE, UK;-
Venue:
Discrete Applied Mathematics
Year:
2011

Citing 4
Cited 0

Logical analysis of numerical data

Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
An Implementation of Logical Analysis of Data

IEEE Transactions on Knowledge and Data Engineering
A Boolean measure of similarity

Discrete Applied Mathematics - Special issue: Discrete algorithms and optimization, in honor of professor Toshihide Ibaraki at his retirement from Kyoto University
Using a similarity measure for credible classification

Discrete Applied Mathematics

Quantified Score

Hi-index	0.04

Visualization

Abstract

In data analysis problems where the data are represented by vectors of real numbers, it is often the case that some of the data-points will have ''missing values'', meaning that one or more of the entries of the vector that describes the data-point is not observed. In this paper, we propose a new approach to the imputation of missing binary values. The technique we introduce employs a ''similarity measure'' introduced by Anthony and Hammer (2006) [1]. We compare experimentally the performance of our technique with ones based on the usual Hamming distance measure and multiple imputation.