Efficient Methods for Dealing with Missing Data in Supervised Learning

This work presents efficient algorithms for dealing with the problem of missing inputs (incomplete feature vectors) during training and recall based on the approximation of the input data distribution using Parzen windows.

Abstract

We present efficient algorithms for dealing with the problem of missing inputs (incomplete feature vectors) during training and recall. Our approach is based on the approximation of the input data distribution using Parzen windows. For recall, we obtain closed form solutions for arbitrary feedforward networks. For training, we show how the backpropagation step for an incomplete pattern can be approximated by a weighted averaged backpropagation step. The complexity of the solutions for training and recall is independent of the number of missing features. We verify our theoretical results using one classification and one regression problem. 1 Introduction The problem of missing data (incomplete feature vectors) is of great practical and theoretical interest. In many applications it is important to know how to react if the At the time of the research for this paper, a visiting researcher at the Center for Biological and Computational Learning, MIT. E-mail: [email protected] ...