Hierarchical Classification Using Contextual Relational Data
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Solution Overview
Problem
Existing methods for classifying data records into hierarchical structures are inefficient when nodes at a desired classification level are similar, leading to increased computational requirements and potential need for human intervention.
Innovation Solution
A classification method that receives an input record with contextual attributes, retrieves relational data from historical records, and determines relevance scores for nodes in a hierarchical structure based on both record content features and contextual information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional classification methods are used for similar nodes, then classification coverage is maintained, but computational efficiency deteriorates and more iterations are required
Solution Approach 1:
The system performs preliminary classification using content features to generate an initial ranking of nodes before applying contextual features. This preliminary action filters the search space, so that contextual information is only applied to a subset of candidate nodes, reducing overall computational requirements while maintaining accuracy for similar nodes
Solution Approach 2:
Contextual features act as an intermediary that bridges content-based similarity and final classification decisions. The system uses contextual information (e.g., user behavior, document metadata) to disambiguate between nodes with similar content features, improving accuracy without requiring exhaustive comparison of all nodes
2Measurement precision
If more records are classified to construct accurate rankings, then classification accuracy improves, but time consumption increases
Solution Approach 1:
The system applies partial action by using contextual features selectively rather than uniformly across all nodes. Contextual information is applied to refine rankings only when content-based features produce ambiguous or similar scores, avoiding unnecessary computational overhead for clearly distinguishable cases while maintaining accuracy where needed
3Reliability
If human intervention is used for ambiguous classification, then classification accuracy improves, but automation level decreases
Solution Approach 1:
The system performs self-service by automatically incorporating contextual features into the classification process without requiring human intervention. The contextual information (user preferences, historical data, metadata) enables the system to autonomously resolve ambiguities that would traditionally require human judgment, maintaining high accuracy while preserving automation
Data Source
AI summary
A method for classifying records by relevance to nodes of a hierarchical structure representative of a classification scheme for different classification options. The method includes receiving an input record having a plurality of record content features, and a contextual attribute indicative of a context of the receipt of the input record, retrieving relational data indicative of one or more nodes of the hierarchical structure that are associated with the contextual at-tribute of the received input record, and determining a relevance score for one or more of the nodes of the hierarchical structure to classify the input record. The relevance score of each node is determined in dependence on a comparison of the plurality of record content features of the input record relative to the classification option represented by said node, and further wherein the relevance score of each node depends on the retrieved relational data.