Automated Data Classification Engine Using Compound Feature Vectors
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Solution Overview
Problem
Existing systems for automated classification of large data sets, particularly unstructured and semi-structured documents, face challenges in efficiently integrating search capabilities with text analysis and require significant human intervention for labeling and training, leading to inconsistencies and inefficiencies.
Innovation Solution
An automated or semi-automated system that converts unclassified data sets into graphic and text compound data sets, uses machine learning classifiers to improve classification performance, generates self-training data sets, and detects inconsistencies, allowing for automated labeling and classification with reduced human effort and improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated classification systems are used for large unstructured data sets, then productivity is improved, but measurement precision deteriorates due to inconsistencies in automated labeling
Solution Approach 1:
The system implements feedback loops where classification results are continuously evaluated and used to refine labeling rules. The classification engine processes documents and feeds results back to update the labeling system, improving accuracy over time while maintaining automated processing speed.
Solution Approach 2:
The system performs preliminary actions by pre-processing documents and extracting features before classification. Training data is prepared in advance with standardized labeling rules, so that when actual classification occurs, the system can operate efficiently with consistent precision.
2Measurement precision
If manual labeling is used for training data, then measurement precision is improved, but loss of time increases due to significant human intervention required
Solution Approach 1:
The system performs self-service by automatically generating training data through its own classification operations. The labeling system uses pre-defined rules and extracted features to create labeled training examples without requiring manual human intervention, thus maintaining precision while eliminating time loss.
Solution Approach 2:
The system merges the classification and labeling functions into a unified process. The same feature extraction and analysis mechanisms used for classification are leveraged to generate training labels automatically, combining multiple functions into one efficient system that eliminates the need for separate manual labeling steps.
3Ease of operation
If existing classification systems are used, then ease of operation is maintained, but adaptability deteriorates due to inability to handle inconsistent data sets from multiple sources
Solution Approach 1:
The labeling system is designed with universal functionality to handle multiple data types and formats from diverse sources. It can process structured data, semi-structured documents, and unstructured text using the same core mechanisms, making the system adaptable to various data sources while maintaining ease of operation through a unified interface.
Solution Approach 2:
The system implements dynamic adaptability by allowing labeling rules and parameters to be adjusted based on the characteristics of incoming data. The classification engine can dynamically adapt to different data sources and formats, and these adaptations are automatically incorporated into the labeling process without requiring system reconfiguration or sacrificing usability.
4Loss of time
If automated labeling systems are implemented, then loss of time is reduced, but manufacturing precision deteriorates due to inconsistencies in automated data generation
Solution Approach 1:
The system maintains precision by carefully controlling and optimizing parameters in the automated labeling process. Feature extraction parameters, similarity thresholds, and classification criteria are tuned to ensure consistent results. The system monitors and adjusts these parameters to maintain manufacturing precision while benefiting from automated processing speed.
Data Source
AI summary
A fully or semi-automated, integrated learning, labeling and classification system and method have closed, self-sustaining pattern recognition, labeling and classification operation, wherein unclassified data sets are selected and converted to an assembly of graphic and text data forming compound data sets that are to be classified. By means of feature vectors, which can be automatically generated, a machine learning classifier is trained for improving the classification operation of the automated system during training as a measure of the classification performance if the automated labeling and classification system is applied to unlabeled and unclassified data sets, and wherein unclassified data sets are classified automatically by applying the machine learning classifier of the system to the compound data set of the unclassified data sets.