Content Classification Using Iterative Learning and User Feedback
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Solution Overview
Problem
Existing document classification systems face inefficiencies in accuracy and require significant human effort due to reliance on operator experience, leading to variations in classifier performance and the need for large amounts of training data, which can be burdensome.
Innovation Solution
A method utilizing machine learning with a graphical user interface that iteratively refines a learning model through repeated classification and evaluation, allowing for efficient classification of content using a small initial dataset and user feedback to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning is performed using a large amount of training data to improve classifier accuracy, then classification accuracy is improved, but user burden and time consumption increase
Solution Approach 1:
The system performs preliminary classification using an initial learning model before user feedback is collected. This allows the classification process to start with minimal training data, and the model is subsequently refined through iterative updates based on user corrections, rather than requiring all training data to be prepared in advance
Solution Approach 2:
The system implements a feedback mechanism where user corrections to classification results are collected and used to update the learning model. This iterative feedback loop allows the model to improve accuracy over time without requiring a large initial training dataset, as each user interaction provides targeted learning opportunities
2Measurement precision
If manual classification is performed by operators to ensure accuracy, then classification accuracy is improved, but productivity decreases
Solution Approach 1:
The learning model performs self-updates by automatically incorporating user feedback to improve its own classification performance. This eliminates the need for continuous manual reclassification while maintaining improving accuracy, as the system serves itself through automated learning from interactions
Solution Approach 2:
The system performs preliminary automated classification to handle the bulk of content, providing initial results that are then refined through user feedback. This combines the speed of automated classification with the accuracy of human review only where needed, rather than requiring manual review of all content
3Productivity
If a single machine learning pass is performed to classify content, then productivity is improved, but classification accuracy decreases
Solution Approach 1:
The classification process continues iteratively rather than completing in a single pass. The learning model continuously updates its performance based on user feedback, maintaining productive automated classification while progressively improving accuracy through repeated refinement cycles
Solution Approach 2:
The system transitions from a static single-pass classification approach to a dynamic iterative process where the learning model adapts and evolves based on ongoing user interactions. The classification accuracy dynamically improves over time while maintaining high productivity through automated processing
Data Source
AI summary
A novel content classification method is provided. A content classification method using machine learning for a learning model and a classifier fabrication method are provided. In Step 1, a data set containing a plurality of contents is acquired. Learning labels are attached to m contents, and the learning labels are not attached to the remaining contents. In Step 2, a first learning model is created by machine learning using the m contents. In Step 3, judgment labels are attached to the plurality of contents using the first learning model and are displayed on a GUI. In Step 4, new learning labels are attached to k contents in the plurality of contents. In Step 5, a second learning model is created by the machine learning using the k contents. In Step 6, judgment labels are attached to the plurality of contents using the second learning model and are displayed on the GUI.