Adaptive Machine Learning Model Selection by Data Complexity
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Solution Overview
Problem
Existing machine learning models struggle to adapt to changing processes over time, leading to convergence issues when retrained, especially when data complexity increases.
Innovation Solution
An adaptive machine learning model selection system that integrates data complexity metrics and user goals, using a computing device to generate models, determine complexity gaps, and select models based on these gaps to ensure continuous adaptability and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning models are retrained on increasingly complex data, then the model can handle more complex processes, but the model becomes unable to converge
Solution Approach 1:
The system dynamically selects models based on real-time complexity metrics of the input data. Instead of using a fixed model, the system adapts the model selection to the current data complexity level, ensuring that the selected model can successfully converge on the given data while maintaining adaptability to increasingly complex processes over time
2Measurement precision
If the system uses complex models to handle complex data, then the model can capture more patterns, but the system complexity increases
Solution Approach 1:
The system changes the parameter of model selection based on data complexity metrics. By adjusting which model is selected according to the complexity level of the input data, the system achieves high pattern recognition accuracy for complex data while avoiding the unnecessary complexity of always using the most sophisticated model
Solution Approach 2:
The system performs preliminary assessment of data complexity using complexity metrics before model selection. This preliminary action allows the system to pre-determine which model is most appropriate for the given data, avoiding the need to use overly complex models and reducing overall system complexity while maintaining high accuracy
Data Source
AI summary
The apparatus employs adaptive machine learning for model selection based on data complexity and user goals. It consists of a processor and memory. Initially, it creates a first model from a dataset and analytic goals. Then, it determines a complexity metric for another dataset. Using a feature learning algorithm, it extracts candidate features from the second dataset. From these features, it generates a second model. The device assesses this model's performance using a third dataset and selects it based on its relation to the complexity gap.