Independent Classifier Ensemble for Network Cheating Detection
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Solution Overview
Problem
Single classifiers in machine learning yield low accuracy in classification results, particularly in identifying network cheating data, which affects search engine efficiency and user experience.
Innovation Solution
A method and apparatus that input data into multiple independent target classifiers, each constructed using different training samples or algorithms, and dynamically update the number of classifiers based on accuracy and construction time to obtain a comprehensive classification result.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single classifier is used for classification, then the device complexity is low, but the classification accuracy is not high
Solution Approach 1:
The patent divides the classification task into multiple independent classifiers, each responsible for a portion of the classification work. Instead of using one complex classifier, the system segments the classification function across M independent classifiers (where M≥2), allowing each classifier to specialize in specific aspects of the classification problem, thereby improving overall accuracy while managing complexity through modular design
Solution Approach 2:
The patent combines the results from multiple independent classifiers through a result integration module. The classification results from M different classifiers are merged using weighted aggregation, where each classifier's result is combined based on its predicted outcome and weight, producing a final integrated classification result that leverages the strengths of multiple classifiers
2Measurement precision
If multiple classifiers are used to improve classification accuracy, then the classification accuracy improves, but the system complexity increases
Solution Approach 1:
The patent implements dynamic classifier management through an update module that continuously monitors classifier performance and adjusts the classifier pool. Classifiers are dynamically added or removed based on their classification accuracy and construction time metrics, allowing the system to adapt its complexity level according to performance requirements rather than maintaining a fixed number of classifiers
Solution Approach 2:
The patent changes the parameters used to evaluate and select classifiers, specifically using classification accuracy and construction time as key parameters. The update module adjusts the classifier configuration by selecting classifiers that optimize the balance between accuracy and construction time, allowing the system to adapt its complexity based on these parameter changes
3Measurement precision
If classifiers are updated frequently to maintain accuracy, then the classification accuracy is maintained, but the time consumption increases
Solution Approach 1:
The patent applies partial action by updating only a subset of classifiers rather than retraining all classifiers frequently. The update module selectively adds or removes classifiers based on performance metrics, performing partial updates that maintain accuracy while minimizing the time and computational resources required compared to complete system retraining
Data Source
AI summary
The present invention provides a method and apparatus for classification. In the embodiments of the present invention, data to be predicted is input into M target classifiers respectively, so as to obtain the predicted result output by each target classifier of the M target classifiers, where M is an integer greater than or equal to 2, and each of the target classifiers is independent of another, so that a classification result of the data can be obtained according to the predicted result output by each of the target classifiers and a prediction weight of each of the target classifiers; and since each target classifier of the M target classifiers is independent of another, the classification result of the data can be obtained by making full use of the classification capability of each target classifier, thus improving the accuracy of the classification result.