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arXiv · 2512.00298

Challenges of Heterogeneity in Big Data: A Comparative Study of Classification in Large-Scale Structured and Unstructured Domains

González Trigueros Jesús Eduardo·Alonso Sánchez Alejandro·Muñoz Rivera Emilio·Peñarán Prieto Mariana Jaqueline·Mendoza González Camila Natalia
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Abstract

This study analyzes the impact of heterogeneity ("Variety") in Big Data by comparing classification strategies across structured (Epsilon) and unstructured (Rest-Mex, IMDB) domains. A dual methodology was implemented: evolutionary and Bayesian hyperparameter optimization (Genetic Algorithms, Optuna) in Python for numerical data, and distributed processing in Apache Spark for massive textual corpora. The results reveal a "complexity paradox": in high-dimensional spaces, optimized linear models (SVM, Logistic Regression) outperformed deep architectures and Gradient Boosting. Conversely, in text-based domains, the constraints of distributed fine-tuning led to overfitting in complex models, whereas robust feature engineering -- specifically Transformer-based embeddings (ROBERTa) and Bayesian Target Encoding -- enabled simpler models to generalize effectively. This work provides a unified framework for algorithm selection based on data nature and infrastructure constraints.

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González Trigueros Jesús Eduardo, Alonso Sánchez Alejandro, Muñoz Rivera Emilio, Peñarán Prieto Mariana Jaqueline, Mendoza González Camila Natalia. 2025-11-29. Challenges of Heterogeneity in Big Data: A Comparative Study of Classification in Large-Scale Structured and Unstructured Domains. https://arxiv.org/abs/2512.00298

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