Trinary Decision Trees for handling missing data
It addresses missing data handling in decision trees, which is a common issue in machine learning applications, but is incremental as it builds on existing approaches.
This paper tackled the problem of handling missing data in decision tree algorithms by introducing the Trinary decision tree, which outperformed existing methods in MCAR settings, especially out-of-sample, and a hybrid TrinaryMIA tree showed robust performance across all missingness types.
This paper introduces the Trinary decision tree, an algorithm designed to improve the handling of missing data in decision tree regressors and classifiers. Unlike other approaches, the Trinary decision tree does not assume that missing values contain any information about the response. Both theoretical calculations on estimator bias and numerical illustrations using real data sets are presented to compare its performance with established algorithms in different missing data scenarios (Missing Completely at Random (MCAR), and Informative Missingness (IM)). Notably, the Trinary tree outperforms its peers in MCAR settings, especially when data is only missing out-of-sample, while lacking behind in IM settings. A hybrid model, the TrinaryMIA tree, which combines the Trinary tree and the Missing In Attributes (MIA) approach, shows robust performance in all types of missingness. Despite the potential drawback of slower training speed, the Trinary tree offers a promising and more accurate method of handling missing data in decision tree algorithms.