Machine Learning Techniques for Solving Classification Problems with Missing Input Data

Missing input data is a common drawback in many real-life pattern classification scenarios. The ability of missing data handling has become a fundamental requirement for pattern classification because an inappropriate treatment may cause large errors or false results on classification. The absence of certain values for relevant data attributes can seriously affect the accuracy of classification results. The goal of this paper is to analyze the missing data problem in pattern classification, and to make a descriptive survey of machine learning solutions for performing incomplete pattern classification tasks.