A new imputation method for incomplete binary data |
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Authors: | Munevver Mine Subasi Martin Anthony |
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Institution: | a Department of Mathematical Sciences, Florida Institute of Technology, 150 W. University Blvd., Melbourne, FL 32901, USAb RUTCOR, Rutgers Center for Operations Research, 640 Bartholomew Road, Piscataway, NJ 08854, USAc Department of Mathematics, London School of Economics and Political Sciences, Houghton Street, London WC2A 2AE, UK |
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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. |
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Keywords: | Imputation Boolean similarity measure |
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