Cost-Sensitive Auto-Encoder Pre-Training for Deep Learning Classification
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Solution Overview
Problem
Existing cost-sensitive classification algorithms fail to effectively account for varying costs of misclassification errors, particularly in real-world applications where some errors have severe consequences, as they either ignore cost information during training or introduce errors by manipulating data or models.
Innovation Solution
A cost-aware training approach that incorporates cost information in both the pre-training and fine-tuning stages using a cost-sensitive auto-encoder, modifying the objective function to learn and store cost information, thereby improving the performance of deep learning classification systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cost information is ignored during training, then training process is simple, but classification performance cannot account for varying costs of misclassification errors
Solution Approach 1:
The patent applies preliminary action by pre-training the auto-encoder with cost-sensitive objective function before the main classification task. The cost information is incorporated during the pre-training phase, allowing the model to learn cost-aware representations beforehand. This resolves the contradiction by preparing the model in advance to handle cost-sensitive classification without complicating the main training process.
Solution Approach 2:
The training process is segmented into two distinct stages: pre-training with cost-sensitive auto-encoder and fine-tuning with classification objective. By separating these functions, the patent achieves cost-aware classification performance while keeping each stage's complexity manageable. The pre-training stage handles cost information incorporation, while the fine-tuning stage focuses on classification accuracy.
2Reliability
If cost information is incorporated only during testing, then training remains simple, but the model cannot optimize for cost-sensitive classification
Solution Approach 1:
The patent incorporates cost information during the pre-training phase rather than only during testing. This preliminary action allows the model to learn cost-aware features and representations in advance, which then guide the fine-tuning process. As a result, the model achieves better cost-sensitive classification accuracy without requiring extensive re-training, thus managing training time effectively.
3Reliability
If training data is manipulated to reflect cost sensitivity, then cost-sensitive classification is achieved, but data manipulation introduces errors and complexity
Solution Approach 1:
The patent introduces a cost-sensitive auto-encoder as an intermediary component between the raw data and the classification model. This intermediary learns to represent data in a cost-aware manner through its objective function, which incorporates cost information. By using this intermediary representation rather than directly manipulating raw data, the patent achieves cost-sensitive classification without introducing errors through data manipulation.
Solution Approach 2:
The patent replaces mechanical data manipulation approaches with a neural network-based solution. Instead of directly modifying training data to reflect cost sensitivity (which introduces errors), the system uses a cost-sensitive auto-encoder that learns cost-aware representations through its objective function. This substitution of mechanical data processing with neural network learning eliminates data manipulation errors while maintaining cost-sensitive performance.
Data Source
AI summary
Classification techniques are disclosed that take into account the “cost” of each type of classification error for minimizing total cost of errors. In one example embodiment, a pre-trained cost-sensitive auto-encoder can be used in combination with a training (fine-tuning) stage for cost-sensitive deep learning. Thus, cost information is effectively combined with deep learning by modifying the objective function in the pre-training phase. By minimizing the modified objective function, the auto-encoder not only tries to capture underlying pattern, it further “learns” the cost information and “stores” it in the structure. By later fine-tuning at the training stage, the classification system yields improved performance (lower cost) than a typical classification system that does not take cost information into account during pre-training.