BEST : A decision tree algorithm that handles missing values
This addresses a common data analysis challenge for researchers and practitioners dealing with incomplete datasets, though it appears incremental in nature.
The paper tackles the problem of analyzing datasets with missing values by developing a new decision tree algorithm that allows user-guided data partitioning, resulting in slightly more accurate and more interpretable classifications without requiring imputation or pre-processing.
The main contribution of this paper is the development of a new decision tree algorithm. The proposed approach allows users to guide the algorithm through the data partitioning process. We believe this feature has many applications but in this paper we demonstrate how to utilize this algorithm to analyse data sets containing missing values. We tested our algorithm against simulated data sets with various missing data structures and a real data set. The results demonstrate that this new classification procedure efficiently handles missing values and produces results that are slightly more accurate and more interpretable than most common procedures without any imputations or pre-processing.