Missing or Inapplicable: Treatment of Incomplete Continuous-valued Features in Supervised Learning

Real-world data are often riddled with data quality problems such as noise, outliers and missing values, which present significant challenges for supervised learning algorithms to effectively classify them. This paper explores the ill-effects of inapplicable features on the performance of supervised learning algorithms. In particular, we highlight the difference between missing and inapplicable feature values. We argue that the current approaches for dealing with missing values, which are mostly based on single or multiple imputation methods, are insufficient to handle inapplicable features, especially those that are continuous valued. We also illustrate how current tree-based and kernelbased classifiers can be adversely affected by the presence of such features if not handled appropriately. Finally, we propose methods to extend existing tree-based and kernel-based classifiers to deal with the inapplicable continuous-valued features.