Dynamic Logistic Ensembles with Recursive Probability and Automatic Subset Splitting for Enhanced Binary Classification

TL;DR

This paper introduces a dynamic logistic ensemble method with recursive probability calculations and automatic subset splitting to improve binary classification, especially in datasets with internal clusters, balancing interpretability and performance.

Contribution

The work develops a novel ensemble approach that automatically partitions data and uses recursive probability, enhancing scalability and interpretability over traditional ensemble methods.

Findings

Significant performance improvements on simulated datasets.

Effective handling of datasets with internal cluster structures.

Maintains interpretability while achieving competitive accuracy.

Abstract

This paper presents a novel approach to binary classification using dynamic logistic ensemble models. The proposed method addresses the challenges posed by datasets containing inherent internal clusters that lack explicit feature-based separations. By extending traditional logistic regression, we develop an algorithm that automatically partitions the dataset into multiple subsets, constructing an ensemble of logistic models to enhance classification accuracy. A key innovation in this work is the recursive probability calculation, derived through algebraic manipulation and mathematical induction, which enables scalable and efficient model construction. Compared to traditional ensemble methods such as Bagging and Boosting, our approach maintains interpretability while offering competitive performance. Furthermore, we systematically employ maximum likelihood and cost functions to facilitate the analytical derivation of recursive gradients as functions of ensemble depth. The effectiveness of the proposed approach is validated on a custom dataset created by introducing noise and shifting data to simulate group structures, resulting in significant performance improvements with layers. Implemented in Python, this work balances computational efficiency with theoretical rigor, providing a robust and interpretable solution for complex classification tasks with broad implications for machine learning applications. Code at https://github.com/ensemble-art/Dynamic-Logistic-Ensembles