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[2602.16876] ML-driven detection and reduction of ballast information in multi-modal datasets
Summary
This paper presents a framework for detecting and reducing ballast information in multi-modal datasets, enhancing machine learning efficiency by pruning redundant features.
Why It Matters
As datasets grow in size and complexity, identifying and eliminating low-utility information becomes crucial for optimizing machine learning models. This research provides a structured approach to improve model performance and reduce computational costs, making it highly relevant for data scientists and machine learning practitioners.
Key Takeaways
- Introduces a framework for identifying and reducing ballast in datasets.
- Demonstrates potential to prune over 70% of features with minimal impact on performance.
- Proposes a novel Ballast Score for cross-modal feature pruning.
- Identifies distinct types of ballast, aiding in targeted reduction strategies.
- Offers practical guidance for more efficient machine learning pipelines.
Computer Science > Machine Learning arXiv:2602.16876 (cs) [Submitted on 18 Feb 2026] Title:ML-driven detection and reduction of ballast information in multi-modal datasets Authors:Yaroslav Solovko View a PDF of the paper titled ML-driven detection and reduction of ballast information in multi-modal datasets, by Yaroslav Solovko View PDF HTML (experimental) Abstract:Modern datasets often contain ballast as redundant or low-utility information that increases dimensionality, storage requirements, and computational cost without contributing meaningful analytical value. This study introduces a generalized, multimodal framework for ballast detection and reduction across structured, semi-structured, unstructured, and sparse data types. Using diverse datasets, entropy, mutual information, Lasso, SHAP, PCA, topic modelling, and embedding analysis are applied to identify and eliminate ballast features. A novel Ballast Score is proposed to integrate these signals into a unified, cross-modal pruning strategy. Experimental results demonstrate that significant portions of the feature space as often exceeding 70% in sparse or semi-structured data, can be pruned with minimal or even improved classification performance, along with substantial reductions in training time and memory footprint. The framework reveals distinct ballast typologies (e.g. statistical, semantic, infrastructural), and offers practical guidance for leaner, more efficient machine learning pipelines. Comments: Subjects:...
