Pattern Recognition with Partly Missing Data

An experimental comparison of several simple inexpensive ways of doing pattern recognition when some data elements are missing (blank) is presented. Pattern recognition methods are usually designed to deal with perfect data, but in the real world data elements are often missing due to error, equipment failure, change of plans, etc. Six methods of dealing with blanks are tested on five data sets. Blanks were inserted at random locations into the data sets. A version of the K-nearest neighbor technique was used to classify the data and evaluate the six methods. Two methods were found to be consistently poor. Four methods were found to be generally good. Suggestions are given for choosing the best method for a particular application.