Hierarchical Internet Content Classification via Grouped Categorization
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Solution Overview
Problem
Existing Internet security systems face challenges in effectively categorizing and managing Internet content due to the lack of reliable classification methods, especially when classification scores are below a threshold, leading to potential misclassification and reduced security efficacy.
Innovation Solution
A method and apparatus that classify Internet content data using classifiers to identify content classes and determine classification scores, assigning content data to a selected content group if individual class scores do not exceed a threshold, utilizing a hierarchical approach to ensure more generic content groups are applied for enhanced security and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If strict classification thresholds are applied to ensure accuracy, then classification reliability is improved, but content data with scores below the threshold cannot be classified and may be misclassified or lost
Solution Approach 1:
The classification system is segmented into multiple hierarchical levels. When content data does not meet the threshold for a specific content class, it is automatically evaluated against broader content groups. This segmentation ensures that all content data receives a classification label, preventing information loss while maintaining reliability through tiered evaluation.
Solution Approach 2:
The system adds a hierarchical dimension to the classification process by introducing content groups as a broader category above content classes. This dimensional expansion allows content data to be classified at multiple levels of granularity, ensuring comprehensive coverage without compromising the strict threshold requirements for specific classes.
2Measurement precision
If multiple classifiers are used to identify content classes, then classification granularity and detail are improved, but system complexity increases
Solution Approach 1:
The classification functionality is segmented into modular components: multiple content class classifiers and content group classifiers. Each classifier operates independently with its own threshold, allowing the system to achieve high granularity through multiple specialized classifiers while managing complexity through functional segmentation and hierarchical organization.
3Adaptability or versatility
If content data is assigned to broader content groups when class-specific thresholds are not met, then classification coverage is improved, but classification precision may be reduced
Solution Approach 1:
The classification system segments labels into two distinct hierarchical levels: specific content classes and broader content groups. This segmentation allows the system to maintain high precision for content that clearly meets class-specific thresholds while providing adaptive coverage through content groups for ambiguous cases, preserving precision where possible and extending coverage where needed.
Solution Approach 2:
By introducing content groups as a broader hierarchical dimension, the system enables multi-level classification. Content data can be classified with high precision at the content class level when thresholds are met, or alternatively classified at the content group level for broader coverage, allowing the system to adapt precision and coverage based on confidence levels without compromising either objective.
Data Source
AI summary
In one embodiment, a device in a network classifies Internet content data using one or more classifiers to identify a plurality of content classes for the content data. Each content class has a corresponding classification score based on the classification. The device determines whether any of the classification scores exceed a threshold level. The device identifies a set of content groups, where each of the plurality of content classes is associated with one of the content groups. The device associates the content data with a selected one of the content groups based on a determination that the classification scores for the plurality of content classes do not exceed the threshold level.