Missing Value Imputation Based on MINNS-SVM

Missing value processing is an unavoidable problem of data pre-processing in the field of Machine learning. Most traditional missing value imputation methods are based on probability distribution and the likes, which might not be suitable for high-dimensional data. Inspired by many unique advantages of Support Vector Machine (SVM) in the high-dimensional model, this paper proposes the missing value imputation based on the nearest neighbors similarity and support vector machine. Four commonly used data sets in the UCI machine learning database are adopted in the experiment, with experimental results showing that MINNS-SVM is effective.