Classifier Learning Unit for Accuracy Maintenance
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Solution Overview
Problem
It is challenging to maintain the classification accuracy of classifiers in closed environments without frequent updates, as collecting labeled learning data is difficult and applying updated classifiers is cumbersome, especially in scenarios like closed security domains.
Innovation Solution
A creating apparatus that includes a classifier learning unit, a time series change learning unit, and a predicting unit, which learns and predicts the classification criterion over time using past data to forecast future classifier performance without relying on continuous labeled learning data updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If labeled learning data is collected frequently to update the classifier, then classification accuracy is maintained, but the complexity and difficulty of data collection increases
Solution Approach 1:
The system performs preliminary actions by collecting and storing unlabeled data continuously in the background, and pre-processing it to extract features. When update is needed, the classifier is already prepared with recent data patterns, reducing the need for frequent manual labeled data collection while maintaining accuracy
Solution Approach 2:
An intermediary mechanism (unlabeled data buffer and feature extraction system) is introduced between data collection and classifier training. This intermediary allows the system to adapt to concept drift using automatically extracted features from recent unlabeled data, reducing dependency on frequent labeled data collection
2Reliability
If the classifier is updated frequently with new labeled learning data, then classification accuracy is maintained, but the time and resources required for updates increase
Solution Approach 1:
The system continuously pre-processes recent unlabeled data to extract features and maintains a buffer of recent data patterns. This preliminary preparation allows rapid classifier updates when needed, as the system already has recent data representations ready, significantly reducing update time
Solution Approach 2:
The system changes the parameter of data freshness by incorporating a time-based weighting mechanism that gives higher importance to recent data patterns. This allows the classifier to adapt to concept drift by focusing on recent unlabeled data characteristics without requiring extensive retraining on old labeled data
3Reliability
If an updated classifier is distributed periodically, then classification accuracy is maintained in open environments, but applying updates in closed environments becomes difficult
Solution Approach 1:
The system implements self-service by automatically adapting to concept drift using recent unlabeled data from its own operation environment. The classifier can be updated autonomously by the system itself using locally available data patterns, eliminating the need for external update distribution and making it fully operational in closed environments
Solution Approach 2:
The system introduces dynamics by enabling the classifier to adapt continuously to changing data patterns in its specific environment. Rather than static periodic updates, the system dynamically adjusts to concept drift using recent unlabeled data, making it adaptable to both open and closed environments without requiring external update mechanisms
4Reliability
If expert labeling is performed to obtain labeled learning data, then classification accuracy is improved, but the productivity and speed of data preparation decreases
Solution Approach 1:
The system performs self-service by automatically extracting features and identifying data patterns from unlabeled data without requiring expert labeling. This automated feature extraction process maintains classification accuracy by capturing essential characteristics while dramatically increasing data preparation speed and productivity
Solution Approach 2:
The system substitutes the mechanical process of expert labeling with an automated computational process of feature extraction. Instead of manual expert analysis, the system uses algorithmic feature extraction from unlabeled data, replacing the slow manual process with a fast automated one while maintaining the quality of classification
Data Source
AI summary
In a classifier whose classification accuracy is maintained without frequently collecting labeled learning data, a learning unit learns a classification criterion of a classifier at each time point in the past until the present and learns a time series change of the classification criterion by using data for learning to which a label is given and that is collected until the present. A classifier creating unit predicts a classification criterion of a future classifier and creates a classifier that outputs a label representing an attribute of input data by using the learned classification criterion and time series change.