Anomaly Analysis System Model Reselection for Unsatisfactory Training Data
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Solution Overview
Problem
Existing analytics systems are inadequate in accurately detecting anomalies when trained with unsatisfactory data, as they are sensitive to outliers and fail to provide reliable predictive models.
Innovation Solution
An anomaly analysis system that reselects models using a lower Mean Absolute Error (MAE) score when unsatisfactory training data is detected, allowing for more accurate anomaly detection by switching to a model less sensitive to outliers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a predictive model is trained with unsatisfactory training data, then the model can be generated quickly, but the anomaly detection accuracy deteriorates
Solution Approach 1:
The system dynamically selects between different model training approaches based on data quality assessment. When unsatisfactory training data is detected (through stationarity tests or zero-value thresholds), the system switches to alternative model selection strategies such as using pre-trained models or adjusting training parameters, thereby adapting the modeling process to data conditions while maintaining both speed and accuracy
Solution Approach 2:
The system changes training parameters or model architecture based on data quality indicators. When unsatisfactory data is detected, the system modifies training hyperparameters, selects different model types, or adjusts preprocessing steps to compensate for data quality issues, ensuring accurate anomaly detection without sacrificing generation speed
2Ease of manufacture
If a model is trained with unsatisfactory training data containing outliers, then the training process is simplified, but the predictive performance deteriorates
Solution Approach 1:
The system performs preliminary assessment of training data quality before initiating the training process. By evaluating data stationarity and checking for excessive zero-values in advance, the system identifies unsatisfactory data and prepares alternative modeling approaches, preventing poor predictive performance before it occurs while maintaining training simplicity through automated detection
Solution Approach 2:
The system introduces an intermediary data quality assessment layer between the training data and the model training process. This intermediary evaluates data suitability and mediates by selecting appropriate training strategies or alternative models when data quality is poor, thereby protecting predictive performance without complicating the overall training workflow
3Ease of operation
If the system uses a standard model selection process, then the process is straightforward, but the model performance deteriorates when training data is unsatisfactory
Solution Approach 1:
The model selection process performs self-assessment of training data quality and automatically adjusts its behavior accordingly. The system evaluates its own input data conditions and selects appropriate modeling strategies without external intervention, maintaining operational simplicity while ensuring accurate anomaly detection through adaptive model selection based on detected data quality issues
Data Source
AI summary
An anomaly analysis system generates models capable of more accurately identifying anomalies in data that contains unsatisfactory training data. The anomaly analysis system determines when data contains unsatisfactory training data. When an anomaly is detected in data using an initially selected model, and the data contains unsatisfactory training data, model reselection is performed. The reselected model analyzes the data. The reselected model is used to identify any anomalies in the data based on a data point from the data being outside of a confidence interval related to a predicted point by the reselected model corresponding to the data point.