Graph Grammar Induction for ML Classifier Data Pre-processing
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Solution Overview
Problem
Traditional machine learning methods struggle with classifying minor ontological differences in complex networks due to the complexity of extracting naively represented graph structures, requiring extensive data and computational resources, especially in applications like medical scans and design analysis.
Innovation Solution
A data processing system that employs a probabilistic chunking method paired with a multi-scale random walk based graph exploration approach to efficiently induce grammars for design representations, reducing computational complexity and enabling classification with minimal training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional machine learning methods are used for classifying complex networks, then classification can be performed, but the method requires extensive training data and computational resources
Solution Approach 1:
The patent segments the graph structure into multiple levels of abstraction by extracting grammatical rules at different scales. The system identifies local patterns (nodes, edges, cycles) and combines them into higher-order grammatical structures, allowing classification to operate on compressed representations rather than raw graph data, thus reducing the effective data quantity needed while maintaining classification accuracy.
Solution Approach 2:
The patent introduces grammatical rules as an intermediary representation between raw graph data and classification decisions. These rules serve as a compressed language that captures essential graph structures without requiring the full original data, enabling accurate classification with minimal training data by learning the grammatical patterns rather than memorizing specific graph instances.
2Reliability
If traditional machine learning methods are used for classifying complex networks, then classification can be performed, but computational resources and time are excessively consumed
Solution Approach 1:
The patent segments the graph analysis into hierarchical levels, extracting grammatical rules at different scales from local to global structures. This segmentation allows the system to process graphs by combining results from smaller units rather than analyzing the entire graph at once, significantly reducing computational time while maintaining accuracy through the hierarchical aggregation of grammatical information.
Solution Approach 2:
The patent extracts grammatical rules from the graph structure, separating the essential structural information from the redundant data. By taking out and representing graphs in terms of their grammatical components (nodes, edges, cycles, and their relationships), the system reduces the computational complexity of classification while preserving the essential characteristics needed for accurate classification.
3Manufacturing precision
If grammars are manually created for design representation, then accurate representation can be achieved, but the process is time-consuming and computationally intensive
Solution Approach 1:
The patent implements self-service by enabling the system to automatically induce grammars from graph data without manual intervention. The algorithm autonomously extracts grammatical rules, identifies patterns, and generates the complete grammar representation, eliminating the time-consuming manual process while maintaining the accuracy that would otherwise require expert knowledge and extensive time to achieve.
Solution Approach 2:
The patent performs preliminary extraction of grammatical rules from the graph structure before classification is performed. By pre-computing the grammatical representation through automated rule induction, the system prepares the data in an optimized format that accelerates subsequent classification operations, reducing overall processing time while ensuring accurate representation through systematic extraction of structural patterns.
Data Source
AI summary
A data processing system is configured to pre-process data for a machine learning classifier. The data processing system includes an input port that receives one or more data items, an extraction engine that extracts a plurality of data signatures and structure data, a logical rule set generation engine configured to generate a data structure, select a particular data signature of the data structure, identify each instance of the particular data signature in the data structure, segment the data structure around instances of the particular data signature, identify one or more sequences of data signatures connected to the particular data signature, and generate a logical ruleset. A classification engine executes one or more classifiers against the logical ruleset to classify the one or more data items received by the input port.