This is to presents a study on performance of UCI Hungarian data sets using missing value management techniques. We used bootstrap algorithm with multiple imputation (MI), LOCF, Mean–Mode substitution and IV-for missingness on the reduct file of the dataset to use all 294 instances in the dataset for our experimental input. Five imputed files were generated from the original reduct file in MI technique where from we have taken the average result and created other input files as per requirements for each specified technique, which are studied using two most recognized but opposite in nature approaches for classification, viz. IBPLN and BBP among many of such learning algorithms in the literature [10], but the most well-known among them are back propagation [11], [12], ART [13], and RBF networks [14]. Accuracy for test cases of five imputed files varies from 89.79% to 99.00% by CCR measure, the most recognized benchmarking parameter for judging classification result and performance of the dataset.

Hungarian data sets, CARN, Amelia View, R Statistical platform, Boot strapping, Multiple imputation, LOCF, Mean–Mode substitution, IV-for missingness, online incremental back propagation, Batch back propagation, CCR.

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