Neural Network Hierarchical Data Classification
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Solution Overview
Problem
Managing and processing complex data with multiple parameters across different instances is cumbersome, especially when complex relationships exist between data sets with complex attributes, leading to inefficiencies and errors in data organization and maintenance.
Innovation Solution
Applying machine learning techniques, specifically training artificial neural networks to categorize data using hierarchical structures, allowing for uniform data organization and efficient processing by identifying categories and subcategories through pattern recognition, and providing flexibility in handling different data types and structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is organized in a uniform manner to reduce errors and improve processing efficiency, then data processing accuracy and efficiency are improved, but the complexity of organizing and maintaining complex relationships between data sets increases
Solution Approach 1:
The patent replaces manual data organization and classification mechanisms with machine learning models that automatically learn hierarchical structures from data. The system uses trained models to categorize data into hierarchies without requiring explicit programming of organization rules, thereby reducing the complexity of maintaining data relationships while improving processing accuracy.
Solution Approach 2:
The system enables data to organize itself through automated machine learning processes. The hierarchical structures are learned and maintained automatically by the model without human intervention, allowing the system to self-adapt to new data patterns and maintain accuracy without increasing organizational complexity.
2Productivity
If manual methods are used to organize complex data with multiple parameters and relationships, then flexibility in handling different data types is maintained, but processing speed and efficiency decrease
Solution Approach 1:
Manual data organization operations are replaced with automated machine learning-based classification systems. The model automatically processes complex data with multiple parameters and relationships, dramatically increasing processing speed while the system handles operational simplicity through automated decision-making without requiring manual intervention for each data item.
Solution Approach 2:
The system performs preliminary organization and classification of data structures before actual processing occurs. By pre-training models on data patterns and relationships, the system prepares hierarchical structures in advance, enabling faster processing of new data while maintaining operational simplicity through pre-established classification frameworks.
3Measurement precision
If traditional data processing methods are used to handle complex relationships between data sets, then computational resources are conserved, but processing accuracy and pattern recognition capabilities deteriorate
Solution Approach 1:
The system performs preliminary training of machine learning models on representative data sets to learn hierarchical structures and patterns. This pre-processing phase enables the model to make accurate predictions on new data with reduced computational requirements during actual processing, thereby improving pattern recognition accuracy while managing computational resource consumption during operational phases.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for classifying data objects using machine learning. In an embodiment, an artificial neural network may be trained to identify explained variable values corresponding to data object attributes. For example, the explained variables may be a category and a subcategory with the subcategory having a hierarchical relationship to the category. The artificial neural network may then receive a data record having one or more attribute values. The neural network may then identify a first and second explained variable value corresponding to the one or more attribute values based on the trained neural network model. The first and second explained variable values may then be associated with the data record. For example, if the data record is stored in a database, the record may be updated to include the first and second explained variable values.