A Nonparametric Ensemble Binary Classifier and its Statistical Properties

TL;DR

This paper introduces a nonparametric ensemble classifier combining classification trees and neural networks, demonstrating its statistical robustness and superior performance on real datasets without suffering from the curse of dimensionality.

Contribution

It proposes a novel ensemble classifier with proven statistical properties and broad applicability, outperforming existing models in various high-dimensional classification tasks.

Findings

Proven universal consistency of the classifier

Established upper bounds for key parameters

Demonstrated superior performance on real datasets

Abstract

In this work, we propose an ensemble of classification trees (CT) and artificial neural networks (ANN). Several statistical properties including universal consistency and upper bound of an important parameter of the proposed classifier are shown. Numerical evidence is also provided using various real life data sets to assess the performance of the model. Our proposed nonparametric ensemble classifier doesn't suffer from the `curse of dimensionality' and can be used in a wide variety of feature selection cum classification problems. Performance of the proposed model is quite better when compared to many other state-of-the-art models used for similar situations.