ML-driven detection and reduction of ballast information in multi-modal datasets
This addresses efficiency issues in machine learning pipelines for practitioners dealing with high-dimensional multimodal data, though it appears incremental as it combines existing methods.
The study tackled the problem of redundant low-utility information (ballast) in multimodal datasets by developing a framework for detection and reduction, resulting in pruning over 70% of features in some data types with minimal or improved classification performance and reduced computational costs.
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.