A Novel Index Measured Segmentation Based Imputation Algorithm (with Cross Folds) for Missing Data Imputation

With the rapid increase in the use of databases, missing data make up an important and unavoidable problem in data management and analysis. A most important task when pre-processing the data is, to fill in missing values, smooth out noise and correct inconsistencies. This paper presents the missing value problem in data mining and evaluates some of the methods generally used for missing value imputation. The new method that uses mathematical model for impute missing data. The novel A novel Index Measured segmentation based Imputation Algorithm (with cross folds) for missing data imputation was proposed in this paper. The databases were used to demonstrate the performance of the proposed method. The proposed algorithm is evaluated by extensive experiments and comparison with KNNI, SVMI. The results showed that the proposed algorithm has better performance than the existing imputation algorithms in terms of classification accuracy.