A Cost-Sensitive Deep Belief Network for Imbalanced Classification

Abstract: Imbalanced data with a skewed class distribution are common in many real-world applications. Deep Belief Network (DBN) is a machine learning technique that is effective in classification tasks. However, conventional DBN does not work well for imbalanced data classification because it assumes equal costs for each class. To deal with this problem, cost-sensitive approaches assign different misclassification costs for different classes without disrupting the true data sample distributions. However, due to lack of prior knowledge, the misclassification costs are usually unknown and hard to choose in practice. Moreover, it has not been well studied as to how cost-sensitive learning could improve DBN performance on imbalanced data problems. This paper proposes an evolutionary cost-sensitive deep belief network (ECS-DBN) for imbalanced classification. ECS-DBN uses adaptive differential evolution to optimize the misclassification costs based on training data, that presents an effective approach to incorporating the evaluation measure (i.e. G-mean) into the objective function. We first optimize the misclassification costs, then apply them to deep belief network. Adaptive differential evolution optimization is implemented as the optimization algorithm that automatically updates its corresponding parameters without the need of prior domain knowledge. The experiments have shown that the proposed approach consistently outperforms the state-of-the-art on both benchmark datasets and real-world dataset for fault diagnosis in tool condition monitoring.

• The paper introduces an Evolutionary Cost-Sensitive DBN that integrates adaptive differential evolution with DBNs to automatically optimize misclassification costs.
• It demonstrates significantly higher G-mean values across 58 KEEL benchmark datasets and a real-world gun drilling dataset, outperforming traditional methods.
• The approach highlights the potential for extending cost-sensitive strategies to other deep learning architectures and online learning scenarios for imbalanced data.

A Cost-Sensitive Deep Belief Network for Imbalanced Classification

In addressing the nuanced challenges presented by imbalanced datasets, the research detailed in "A Cost-Sensitive Deep Belief Network for Imbalanced Classification" proposes a novel approach by leveraging Deep Belief Networks (DBNs) integrated with a cost-sensitive learning technique. The central issue with imbalanced data, wherein the class distribution is skewed, is that conventional DBNs perform suboptimally by assuming uniform misclassification costs across classes. This assumption often biases predictions towards the majority class, potentially leading to a detrimental impact in critical applications such as medical diagnostics or fault detection, where minority class instances carry heightened significance.

Key Contributions and Methodology

The paper introduces an Evolutionary Cost-Sensitive Deep Belief Network (ECS-DBN), which innovatively combines adaptive differential evolution with DBNs to automatically optimize the misclassification costs associated with different classes. The framework first determines these costs by maximizing the G-mean—an evaluation metric that accounts for both majority and minority class accuracies—at the expense of assuming known prior information, which is often unavailable in real-world scenarios. The integration of evolutionary algorithms like adaptive differential evolution endows the ECS-DBN with robust search capabilities, dynamically tuning parameters like mutation factor and crossover probability to adaptively optimize model performance.

Through extensive experimentation on 58 KEEL benchmark datasets and a real-world gun drilling dataset, ECS-DBN demonstrates superior performance over other resampling and cost-sensitive methods. The paper systematically evaluates the ECS-DBN using metrics appropriate for imbalanced settings, such as G-mean, accuracy, and precision, showcasing the model's ability to maintain high classification accuracy across both majority and minority classes, while also mitigating the computational inefficiencies typically associated with resampling strategies on larger datasets.

Empirical Findings and Implications

The empirical results are compelling: ECS-DBN consistently exhibits higher G-mean values compared to baseline DBNs and other resampling-empowered DBNs, across a majority of the datasets tested. Statistically, the proposed approach is validated through tests like Wilcoxon paired signed-rank, confirming the significant performance improvements of ECS-DBN over traditional methods. The results manifest the efficacy of incorporating cost-sensitive learning within the DBN framework, revealing ECS-DBN's capability to self-tune misclassification costs, which is particularly critical when coding cost-sensitive tasks without prior cost knowledge is infeasible.

Implications for Future Research

The implications of this study extend towards multiple avenues in deep learning and classification tasks. For one, it invites further exploration of cost-sensitive methodologies within other deep architectures like Convolutional Neural Networks (CNNs). Given the growing importance of real-time and adaptive learning systems, research could also pivot towards extending the ECS-DBN into an online learning context, where imbalanced data streams are a common occurrence amid concept drifts. Future work might also explore hybrid approaches that integrate cost-sensitive techniques into data preprocessing or feature extraction stages, complementing the algorithmic level modifications presented herein.

In summary, this paper offers a significant methodological advancement for handling imbalanced classifications via cost-sensitive deep learning paradigms. Its contributions provide an empirical and theoretical foundation not only for improved predictive accuracies in minority classes but also for maintaining computational efficiency, presenting a meaningful stride forward in both the design and application of machine learning systems dealing with class imbalance.