Incremental Document Classification for Unknown Classes and Privacy
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Solution Overview
Problem
Existing document classification systems face challenges in accurately identifying unknown classes, adapting to new class information over time, and maintaining privacy while handling sensitive documents across multiple data owners.
Innovation Solution
A document classification system utilizing Class Incremental Learning (CIL) to update classifiers without compromising accuracy, incorporating privacy-preserving training techniques, and enabling local training on diverse datasets to reduce data leakage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If statistical ML techniques like TF-IDF are used to extract keywords for new classifier creation, then customization flexibility is improved, but classification accuracy deteriorates due to limitations of statistical ML algorithms
Solution Approach 1:
The system transitions from statistical ML parameters (TF-IDF weights) to deep learning parameters (neural network weights and activations), fundamentally changing the parameter representation to achieve both customization and high accuracy through the same classifier architecture
2Measurement precision
If deep learning models with linear layers are used for class prediction, then model capacity is improved, but adaptability to new classes deteriorates as models cannot learn incrementally without retraining from ground up
Solution Approach 1:
The system pre-computes and stores activation representations for each class during training, so that when new classes are introduced, the model can immediately compare new activations against stored class representations without requiring full retraining, enabling incremental learning while maintaining deep learning accuracy
Solution Approach 2:
The classification process is segmented into feature extraction (shared across all classes) and class matching (comparing activations against stored class representations), allowing the model to add new classes by simply adding new class representations rather than retraining the entire model
3Measurement precision
If multiple data owners collaborate on training and fine-tuning processes, then model performance is improved, but data privacy deteriorates due to disclosure of sensitive information
Solution Approach 1:
The system introduces trained model parameters and predicted probability outputs as intermediaries between data owners, allowing collaborative model improvement without direct access to each other's sensitive training data, thus maintaining privacy while achieving good performance
Solution Approach 2:
The system extracts only the necessary information (trained parameters and probability outputs) from the training process, separating the collaborative learning benefit from the sensitive data, allowing data owners to participate in model improvement without exposing their raw data
4Adaptability or versatility
If comprehensive frameworks with multiple classifiers are implemented, then classification coverage is improved, but system complexity deteriorates
Solution Approach 1:
The system creates a universal classifier framework where a single classifier architecture can handle multiple classes through the activation-matching mechanism, eliminating the need for separate specialized classifiers for each class and reducing overall system complexity
Data Source
AI summary
A method and processor for classifying documents are provided. Using a classification model with classifiers, items are classified into classes. The method includes acquiring a model for classification, creating a training dataset with items and class labels, and training a new classifier for an additional class not in the original set. This results in a modified model that includes both the original classifiers and the new classifier, allowing for classification into an expanded set of classes. The method involves generating a training dataset, training a new classifier, modifying the classification model, and determining a predicted class for items, including the new class.