BEST : A decision tree algorithm that handles missing values

TL;DR

This paper introduces BEST, a decision tree algorithm designed to effectively handle missing data during classification, outperforming common methods without requiring data imputation.

Contribution

The paper presents a novel decision tree algorithm that allows user-guided data partitioning and directly manages missing values, improving accuracy and interpretability.

Findings

Handles missing data efficiently without imputation

Achieves slightly higher accuracy than traditional methods

Produces more interpretable classification results

Abstract

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.