Neural Network Data Classification Adaptability
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Solution Overview
Problem
Existing data classification technologies face challenges in accurately understanding unstructured data due to their reliance on supervised learning and predefined classification systems, which limits their ability to adapt to user-defined classification systems and handle exponentially increasing data.
Innovation Solution
A computing device equipped with a neural network configured for unsupervised learning is used to identify a user-defined classification system, process unstructured data, and determine categories based on the classification system, with the ability to update the system if initial categorization fails.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised learning classification technology is used, then classification accuracy based on predefined systems is improved, but adaptability to user-defined classification systems deteriorates
Solution Approach 1:
The patent implements a dynamic classification system that can switch between supervised learning mode (using predefined classification systems for high accuracy) and unsupervised learning mode (using user-defined classification systems for high adaptability). The system dynamically adjusts its learning approach based on the specific classification task and user requirements, resolving the contradiction between accuracy and adaptability.
Solution Approach 2:
The patent changes the parameter of learning mode from fixed to variable, allowing the system to transition between supervised and unsupervised learning. By adjusting this parameter based on the classification scenario, the system can optimize for either accuracy or adaptability as needed, or achieve a balance between both.
2Stability of the object's composition
If predefined classification systems are used, then classification consistency is improved, but ability to handle unstructured data deteriorates
Solution Approach 1:
The patent segments the classification process into multiple stages: first using unsupervised learning to identify potential categories from unstructured data, then applying supervised learning with predefined systems to ensure consistent classification. This segmentation allows the system to handle unstructured data effectively while maintaining classification consistency through the predefined system stage.
Solution Approach 2:
The patent introduces an intermediary unsupervised learning stage that bridges the gap between unstructured data and predefined classification systems. This intermediary process extracts meaningful patterns from unstructured data and prepares them for consistent classification by the predefined system, resolving the contradiction between handling unstructured data and maintaining consistency.
3Extent of automation
If clustering technology is used, then automatic grouping is improved, but category consistency with user intentions deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the results of automatic clustering are evaluated against user-defined classification systems. The system uses this feedback to refine and adjust the clustering results, ensuring they align with user intentions. This feedback loop maintains high automation while improving category consistency.
Solution Approach 2:
The patent performs preliminary unsupervised clustering to automatically group data, then applies preliminary filtering and adjustment based on user-defined classification criteria. This preliminary action maintains the automation benefit while ensuring the final categories align with user intentions, resolving the contradiction between automatic grouping and category consistency.
4Measurement precision
If supervised learning classification is used, then classification based on hierarchical structure is improved, but scalability to exponentially increased data deteriorates
Solution Approach 1:
The patent adds the dimension of unsupervised learning capability to the traditional supervised learning approach. By incorporating unsupervised learning methods that can handle high-dimensional unstructured data, the system achieves scalability to exponentially increased data while maintaining hierarchical classification through the integration of predefined classification systems.
Solution Approach 2:
The patent creates a universal classification system that combines both supervised and unsupervised learning capabilities in a single framework. This multi-functional system can handle various data types and scales, from structured hierarchical data to unstructured exponential data growth, resolving the contradiction between hierarchical classification and scalability.
Data Source
AI summary
A device and method for classifying a category of data are provided. The device includes: a memory storing one or more instructions; and at least one processor configured to execute the one or more instructions stored in the memory to cause the processor to: identify a classification system of a category of data comprising a classification criterion of the category of the data and a plurality of keywords; obtain data comprising at least one sentence; and determine at least one category with respect to the at least one sentence of the data based on the classification system of the category of the data using a neural network that performs classification by unsupervised learning.