DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data (2105.02340v1)

Abstract: Despite over two decades of progress, imbalanced data is still considered a significant challenge for contemporary machine learning models. Modern advances in deep learning have magnified the importance of the imbalanced data problem. The two main approaches to address this issue are based on loss function modifications and instance resampling. Instance sampling is typically based on Generative Adversarial Networks (GANs), which may suffer from mode collapse. Therefore, there is a need for an oversampling method that is specifically tailored to deep learning models, can work on raw images while preserving their properties, and is capable of generating high quality, artificial images that can enhance minority classes and balance the training set. We propose DeepSMOTE - a novel oversampling algorithm for deep learning models. It is simple, yet effective in its design. It consists of three major components: (i) an encoder/decoder framework; (ii) SMOTE-based oversampling; and (iii) a dedicated loss function that is enhanced with a penalty term. An important advantage of DeepSMOTE over GAN-based oversampling is that DeepSMOTE does not require a discriminator, and it generates high-quality artificial images that are both information-rich and suitable for visual inspection. DeepSMOTE code is publicly available at: https://github.com/dd1github/DeepSMOTE

• The paper introduces a novel framework that integrates deep learning and SMOTE to enhance synthetic sample generation for imbalanced data.
• It employs an encoder-decoder architecture to transform image data into latent space, leading to more accurate interpolation of minority class features.
• Empirical results demonstrate superior performance over traditional and GAN-based methods, improving metrics like ACSA, GM, and F1 score.

Insights on DeepSMOTE: Integrating SMOTE with Deep Learning to Address Imbalanced Data

The problem of class imbalance remains a persistent challenge in the domain of machine learning, despite decades of research. The paper "DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data" by Dablain, Krawczyk, and Chawla introduces a novel methodology that addresses this challenge through the integration of synthetic minority oversampling techniques with deep learning architectures. This paper provides a comprehensive approach, not only enhancing the ability to generate high-quality synthetic samples, particularly for image data, but also improving classifier performance in the presence of imbalanced datasets.

Overview

DeepSMOTE is presented as an advanced oversampling methodology tailored specifically for deep learning models. Traditionally, class imbalance has been mitigated through either modifications in loss functions or instance resampling techniques. DeepSMOTE targets the latter by improving the generation quality of artificial samples through a combination of deep learning's potent representational capabilities and the proven resampling efficacy of SMOTE.

Key components of DeepSMOTE include:

1. Encoder-Decoder Framework: This component enables the transformation of raw image data into a latent feature space where the SMOTE algorithm can generate synthetic samples. This facilitates meaningful interpolation in feature space, rather than directly on pixel data.
2. SMOTE-based Oversampling: By applying the SMOTE algorithm within the latent space obtained via the encoder, DeepSMOTE effectively respects the geometric properties of minority class distributions, thereby enhancing decision boundaries in classifier models.
3. Enhanced Loss Function: A bespoke loss function incorporating a penalty term ensures that the generated samples maintain high fidelity and enhance class discriminability.

Evaluation and Results

The empirical evaluation of DeepSMOTE was conducted using several challenging datasets, including MNIST, Fashion-MNIST, CIFAR-10, SVHN, and CelebA. The key findings include:

• Empirical Validation: DeepSMOTE consistently outperformed both pixel-based and GAN-based oversampling methodologies across various metrics such as Average Class Specific Accuracy (ACSA), Geometric Mean (GM), and F1 score.
• Robustness: DeepSMOTE demonstrated strong robustness against varying imbalance ratios, an important consideration in real-world applications where data skewness can be severe.
• Stability: The models trained using DeepSMOTE displayed stability across different levels of class imbalance, showcasing its potential reliability for dynamic and high-stakes environments like medical diagnostics or security systems.
• Image Quality: The artificial images generated through DeepSMOTE framework were of high visual quality, comparable to real data, which is a critical factor for applications involving human analysis or image-heavy datasets.

Theoretical and Practical Implications

From a theoretical standpoint, DeepSMOTE bridges the gap between traditional oversampling techniques and modern deep learning models, offering a unified approach that leverages the strengths of both paradigms. Practically, this method opens up pathways for deploying deep learning solutions in domains where class imbalance has historically hindered performance, such as fraud detection, anomaly detection, and biometric recognition.

Future Directions

The authors identify several avenues for future research. These include extending the DeepSMOTE architecture to handle non-image data modalities such as graph data, and integrating it into continuous learning frameworks to address dynamic data environments effectively. Another promising direction is enhancing the system's ability to discriminate between normal and noisy instances, potentially leading to even more refined synthetic data generation.

In conclusion, the DeepSMOTE approach signifies a pivotal step towards more effective and reliable use of deep learning in imbalanced settings. This work not only advances the state-of-the-art in synthetic data generation but also serves as a catalyst for further exploration into hybrid architectures that amalgamate classical and modern techniques in machine learning.