Direct Nonparametric Predictive Inference Classification Trees

TL;DR

This paper introduces D-NPI, a novel classification tree algorithm based on Nonparametric Predictive Inference, which uses a new split criterion called Correct Indication, and demonstrates competitive performance on UCI datasets.

Contribution

The paper presents the first classification tree algorithm entirely based on NPI, using a new split criterion called Correct Indication that does not rely on entropy or other assumptions.

Findings

D-NPI achieves high classification accuracy.

D-NPI produces smaller trees with competitive accuracy.

The algorithm performs well across multiple datasets.

Abstract

Classification is the task of assigning a new instance to one of a set of predefined categories based on the attributes of the instance. A classification tree is one of the most commonly used techniques in the area of classification. In this paper, we introduce a novel classification tree algorithm which we call Direct Nonparametric Predictive Inference (D-NPI) classification algorithm. The D-NPI algorithm is completely based on the Nonparametric Predictive Inference (NPI) approach, and it does not use any other assumption or information. The NPI is a statistical methodology which learns from data in the absence of prior knowledge and uses only few modelling assumptions, enabled by the use of lower and upper probabilities to quantify uncertainty. Due to the predictive nature of NPI, it is well suited for classification, as the nature of classification is explicitly predictive as well. The D-NPI algorithm uses a new split criterion called Correct Indication (CI). The CI is about the informativity that the attribute variables will indicate, hence, if the attribute is very informative, it gives high lower and upper probabilities for CI. The CI reports the strength of the evidence that the attribute variables will indicate, based on the data. The CI is completely based on the NPI, and it does not use any additional concepts such as entropy. The performance of the D-NPI classification algorithm is tested against several classification algorithms using classification accuracy, in-sample accuracy and tree size on different datasets from the UCI machine learning repository. The experimental results indicate that the D-NPI classification algorithm performs well in terms of classification accuracy and in-sample accuracy.