scenarios and UCI datasets with various rates of missingness.'
author:
- |
    Arkopal Choudhury arkopal@live.unc.edu\
    Michael R. Kosorok kosorok@bios.unc.edu\
    Department of Biostatistics\
    University of North Carolina\
    Chapel Hill, NC 27599-7420, USA
bibliography:
- 'Referencias.bib'
title: Missing Data Imputation for Classification Problems
---

Missing Data, K Nearest Neighbors, Grey Theory, Mutual Information, Classification Problem

Introduction
============

Many of the commonly used classification algorithms such as Classification and Regression Trees (CART) [@Breiman84CART] and Random Forests [@breiman2001random] do not have rigorous techniques for handle missing values in training data. Ignoring the datapoints with missing values and running the classification algorithm on complete cases only leads to loss of vital information [@little2002]. The occurrence of missing data is one of the biggest challenges for data scientists solving classification problems in real-world data [@duda2012pattern]. These datasets can come from any walk of life, ranging from medical data [@troyanskaya2001missing] and survey responses to equipment faults and limitations [@le2005estimating]. The reason for missingness can be human error in inputting data, incorrect measurements, non-response to surveys, etc. For example, an industrial database maintained by Honeywell, a company manufacturing and servicing complex equipment, has more than 50% missing data [@lakshminarayan1999imputation] despite regulatory requirements for data collection. In wireless sensor 