Deep Learning Comparison Guide on Structured and Unstructured Datasets with Rare Case

Abstract

Deep learning has emerged as a powerful tool for analyzing both structured and unstructured datasets across diverse domains. However, its performance and suitability vary significantly depending on the data type, representation, and underlying task complexity. This paper presents a comparative study of deep learning approaches applied to structured and unstructured datasets, with special emphasis on rare-case scenarios such as imbalanced data, limited samples, and noisy environments. We review key models, architectures, and training techniques, highlighting their advantages and limitations. Experimental evidence and case references suggest that while structured datasets benefit from tabular-specific models and feature engineering, unstructured datasets rely on advanced representation learning using convolutional and transformer-based architectures. Rare-case handling techniques, including data augmentation, transfer learning, and generative modeling, are also discussed. This comparative guide aims to assist researchers and practitioners in selecting suitable deep learning strategies for specific dataset types and challenges.

Introduction

The paper provides a systematic comparison of how deep learning (DL) techniques are applied to structured and unstructured datasets, and addresses strategies to manage rare-case scenarios, such as imbalanced, limited, or noisy data.

???? Types of Data:

1. Structured Data:

• Definition: Fixed schema; organized in rows and columns (e.g., databases, spreadsheets).

• Examples: Financial records, patient data, transactional logs.

• Challenges: Categorical encoding, feature scaling, data sparsity.

• DL Techniques:

  • Deep Neural Networks (DNNs)

  • TabNet (attention-based model for tabular data)

  • Entity embeddings (for categorical variables)

2. Unstructured Data:

• Definition: No fixed schema; complex formats like text, images, audio, video.

• Examples: Social media posts, emails, medical images, voice recordings.

• Challenges: High dimensionality, semantic understanding, context preservation.

• DL Techniques:

  • CNNs (images)

  • RNNs, LSTMs, Transformers (text, sequences)

  • Vision Transformers (ViTs for images)

?? Rare Case Scenarios:

Rare or challenging data situations include:

• Imbalanced Classes: e.g., fraud detection, rare disease diagnosis.

• Limited Samples: few-shot or one-shot learning.

• Noisy/Incomplete Data: sensor data, social media input.

Key Techniques for Handling Rare Cases:

• SMOTE: Synthetic oversampling for minority classes (can lead to low-quality samples).

• Data Augmentation: e.g., image transformations, synonym replacement in text.

• Transfer Learning: Using pre-trained models to improve learning with limited data.

• GANs: Generative models for creating synthetic training samples.

???? Related Work Summary:

• Structured data was traditionally tackled using classical ML models like Random Forests and XGBoost.

• DL models are now increasingly used for structured data with good performance in certain cases.

• Most DL success has historically been in unstructured data domains like NLP, CV, and speech.

???? Comparison Guide:

???? (Optional) Experimental Insights:

• Performance metrics like accuracy, F1-score, and ROC-AUC can be compared across data types.

• Rare-case datasets (e.g., credit card fraud) can show the impact of techniques like SMOTE or transfer learning.

Conclusion

This paper provides a comparative guide to deep learning techniques across structured and unstructured datasets, with a focus on rare-case challenges. Structured datasets often require specialized architectures such as TabNet or embeddings, while unstructured datasets thrive with CNNs and Transformers. Rare cases demand data-centric solutions like augmentation, GANs, and transfer learning. Future work may include benchmarking across unified datasets and developing hybrid models that combine structured and unstructured representations.

References

[1] Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016. [2] A. Vaswani et al., “Attention is all you need,” Advances in Neural Information Processing Systems (NeurIPS), 2017. [3] S. Arik and T. Pfister, “TabNet: Attentive interpretable tabular learning,” in Proc. AAAI, 2021. [4] C. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” Journal of Big Data, vol. 6, no. 1, 2019. [5] H. He and E. A. Garcia, “Learning from imbalanced data,” IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 9, pp. 1263–1284, 2009

Copyright

Copyright © 2025 Jitendra Kumar Das, Sudhir Panigrahy . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.