{"ID":2835071,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.00298","arxiv_id":"2512.00298","title":"Challenges of Heterogeneity in Big Data: A Comparative Study of Classification in Large-Scale Structured and Unstructured Domains","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.","short_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 numer...","url_abs":"https://arxiv.org/abs/2512.00298","url_pdf":"https://arxiv.org/pdf/2512.00298v1","authors":"[\"González Trigueros Jesús Eduardo\",\"Alonso Sánchez Alejandro\",\"Muñoz Rivera Emilio\",\"Peñarán Prieto Mariana Jaqueline\",\"Mendoza González Camila Natalia\"]","published":"2025-11-29T03:41:17Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.CL\",\"cs.DC\"]","methods":"[\"Transformer\"]","has_code":false}