Using Classifier-Based Nominal Imputation to Improve Machine Learning

Abstract

Many learning algorithms perform poorly when the training data are incomplete. One standard approach involves first imputing the missing values, then giving the completed data to the learning algorithm. However, this is especially problematic when the features are nominal. This work presents “classifier-based nominal imputation” (CNI), an easy-to-implement and effective nominal imputation technique that views nominal imputation as classification: it learns a classifier for each feature (that maps the other features of an instance to the predicted value of that feature), then uses that classifier to predict the missing values of that feature. Our empirical results show that learners that preprocess their incomplete training data using CNI using support vector machine or decision tree learners have significantly higher predictive accuracy than learners that (1) do not use preprocessing, (2) use baseline imputation techniques, or (3) use this CNI preprocessor with other classification algorithms. This improvement is especially apparent when the base learner is instance-based. CNI is also found helpful for other base learners, such as naïve Bayes and decision tree, on incomplete nominal data.