# Semantic-Preserving Feature Partitioning for Multi-View Ensemble Learning

arXiv: https://arxiv.org/abs/2401.06251 · HTML: https://mlsanity.com/paper/2401.06251/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 61 references that are in the map.
- First posted on arXiv 11 Jan 2024.

## Builds on (most cited first, 14 of 14)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [A Survey on Multi-view Learning](https://mlsanity.com/paper/1304.5634/) (2013)
- [A Survey of Multi-View Representation Learning](https://mlsanity.com/paper/1610.01206/) (2016)
- [Deep Partial Multi-View Learning](https://mlsanity.com/paper/2011.06170/) (2020)
- [Feature Selection: A Data Perspective](https://mlsanity.com/paper/1601.07996/) (2016)
- [Challenges of Big Data Analysis](https://mlsanity.com/paper/1308.1479/) (2013)
- [Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review](https://mlsanity.com/paper/1905.02845/) (2019)
- [Curse of Dimensionality for TSK Fuzzy Neural Networks: Explanation and Solutions](https://mlsanity.com/paper/2102.04271/) (2021)
- [Multi-view Laplacian Support Vector Machines](https://mlsanity.com/paper/1307.7024/) (2013)
- [Stacked Penalized Logistic Regression for Selecting Views in Multi-View Learning](https://mlsanity.com/paper/1811.02316/) (2018)
- [Big Data: Challenges, Opportunities and Realities](https://mlsanity.com/paper/1705.04928/) (2017)
- [ASM2TV: An Adaptive Semi-Supervised Multi-Task Multi-View Learning Framework for Human Activity Recognition](https://mlsanity.com/paper/2105.08643/) (2021)
- [Simple stopping criteria for information theoretic feature selection](https://mlsanity.com/paper/1811.11971/) (2018)
- [Noise-Augmented Boruta: The Neural Network Perturbation Infusion with Boruta Feature Selection](https://mlsanity.com/paper/2309.09694/) (2023)

## Built on by (most cited first, 2 of 2)

- [From Embeddings to Equations: Genetic-Programming Surrogates for Interpretable Transformer Classification](https://mlsanity.com/paper/2509.21341/) (2025)
- [Domain-Informed Genetic Superposition Programming: A Case Study on SFRC Beams](https://mlsanity.com/paper/2509.21355/) (2025)

## Abstract (arXiv)

In machine learning, the exponential growth of data and the associated ``curse of dimensionality'' pose significant challenges, particularly with expansive yet sparse datasets. Addressing these challenges, multi-view ensemble learning (MEL) has emerged as a transformative approach, with feature partitioning (FP) playing a pivotal role in constructing artificial views for MEL. Our study introduces the Semantic-Preserving Feature Partitioning (SPFP) algorithm, a novel method grounded in information theory. The SPFP algorithm effectively partitions datasets into multiple semantically consistent views, enhancing the MEL process. Through extensive experiments on eight real-world datasets, ranging from high-dimensional with limited instances to low-dimensional with high instances, our method demonstrates notable efficacy. It maintains model accuracy while significantly improving uncertainty measures in scenarios where high generalization performance is achievable. Conversely, it retains uncertainty metrics while enhancing accuracy where high generalization accuracy is less attainable. An effect size analysis further reveals that the SPFP algorithm outperforms benchmark models by large effect size and reduces computational demands through effective dimensionality reduction. The substantial effect sizes observed in most experiments underscore the algorithm's significant improvements in model performance.