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.
The global volume of digital data continues to expand at an unprecedented pace; earlier projections estimated that it would reach approximately 175 zettabytes (ZB) by the end of 2025 [1]. This exponential growth is reshaping industries and technological infrastructures, demanding ever-larger storage systems and more sophisticated analytical tools. While contemporary research has rightly focused on enhancing storage and processing efficiency through well-established techniques such as data cleansing, deduplication, and imputation [2]- [4], a critical yet underexplored dimension is detection and reduction of ballast information as data that is technically valid but contributes minimal or no analytic value.
In this work, ballast information is defined as a specific subset of unnecessary or redundant data elements that inflate storage requirements, degrade processing efficiency, and may obscure meaningful patterns. Typical examples include features with near-zero variance, static metadata fields, repeated headers in log files, or extremely common but uninformative textual tokens. Unlike broader categories of “useless” information, which may require subjective or domain-specific judgement to detect, ballast information lends itself to systematic identification using statistical or machine learning (ML)-based methods.
The consequences of ballast information are twofold. First, from a storage perspective, ballast data occupies valuable disk and memory space, driving up cloud storage costs and hardware requirements. Second, from an analytics perspective, ballast can introduce noise, increase model complexity, and hinder learning algorithms from extracting signal effectively [5]. Preliminary experiments conducted across four distinct data modalities: structured, semi-structured, unstructured, and sparse datasets, demonstrate that ballast information can comprise between 15% and 40% of the total data volume, depending on the domain and data source. This highlights a substantial and often overlooked inefficiency in modern data pipelines.
Motivating this study is recognition that most data pipelines and ML workflows still prioritize predictive accuracy, often at the expense of resource efficiency [5]. While feature selection and regularization methods do partially address redundant or irrelevant features, they rarely aim to explicitly quantify and systematically remove ballast at the dataset level. This research proposes a paradigm shift, positioning ballast detection and reduction as a first-class analytical objective that complements traditional goals of model performance. This shift is timely, given the growing emphasis on responsible data stewardship, digital sustainability, and privacy-preserving data minimization.
The challenge becomes more complex in multi-modal settings, where data sources span diverse structures and formats. Structured datasets, such as relational tables, may contain ballast in the form of sparse or low-variance columns. Semistructured logs or metadata often carry repeated headers or duplicated status flags that provide negligible informational gain. Unstructured text can be burdened by frequent filler words, boilerplate content, or redundant contextual phrases. Sparse datasets, increasingly prevalent in fields like IoT or bioinformatics, often exhibit entire columns or segments with negligible variability. Detecting ballast in such heterogeneous contexts demands a cross-disciplinary approach that combines statistical measures (e.g., entropy, variance thresholds), semantic analysis (e.g., topic modelling for text), and advanced ML techniques (e.g., SHAP-based feature pruning).
This paper addresses the following research questions:
How can ballast information be formally defined and quantified across diverse data modalities?
What ML-driven techniques can do to effectively detect and reduce ballast while preserving the core analytical value? 3. What are the measurable impacts of ballast removal on storage efficiency and model performance?
To address these questions, the proposed approach integrates statistical modelling, information theory, and machine learning. The methodology builds on entropy and sparsity analysis [6] and introduces a preliminary ballast index as a mathematical construct for estimating the proportion of ballast within a dataset based on variance thresholds, feature redundancy, and information gain. Experimental results indicate that the adoption of ballast thresholds (e.g., variance < 0.05) can effectively isolate non-contributory features. Furthermore, pruning strategies based on SHAP values illustrate how ML explain ability methods can be employed to distinguish genuinely informative features from ballast. This framework is extended across data modalities through the development of a multi-modal taxonomy that categorizes ballast according to its statistical, structural, and semantic characteristics.
In summary, the contributions of this work are fourfold. Fir
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