Prediction Data Processing for High-Dimensional Feature Reduction
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Solution Overview
Problem
Existing financial risk control models face challenges in efficiently processing large datasets with high feature dimensions, leading to inefficiencies and inaccuracies in prediction results due to overfitting and the need for manual intervention in data processing steps.
Innovation Solution
A data processing method that involves obtaining a first prediction dataset, generating a second dataset with reduced feature dimension, performing feature transformation based on configuration information, and inputting the transformed data into a data analysis model to obtain a prediction result, enabling end-to-end modeling without manual configuration of each processing step.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual data processing steps are used for financial risk control models, then flexibility and control over each processing step are improved, but efficiency and accuracy deteriorate due to overfitting and time-consuming manual intervention
Solution Approach 1:
The system performs self-service by automatically selecting features, generating candidate expressions, and optimizing model parameters without manual intervention. The automated modeling process enables the system to serve itself in data processing tasks, improving efficiency while maintaining control through configurable parameters and constraints.
Solution Approach 2:
The system performs preliminary actions by pre-defining feature selection criteria, candidate expression templates, and model configuration options before the actual prediction task. This preparation work is automated and stored as configuration information, enabling rapid execution during prediction while maintaining flexibility.
2Adaptability or versatility
If high feature dimension data is used in financial risk control models, then comprehensive analysis coverage is improved, but prediction accuracy deteriorates due to overfitting
Solution Approach 1:
The system extracts only the most relevant features from the high-dimensional data through automated feature selection. By identifying and extracting key features based on their predictive power and relevance to the target variable, the system reduces dimensionality while maintaining comprehensive analysis coverage, thereby preventing overfitting.
Solution Approach 2:
The system changes parameters by transforming the original high-dimensional features into optimized feature representations through candidate expression generation and selection. This parameter transformation process adjusts feature dimensions and relationships to achieve better generalization performance while preserving essential information.
3Reliability
If comprehensive feature sets are used in prediction models, then model completeness is improved, but processing complexity and time consumption increase
Solution Approach 1:
The system performs preliminary feature selection and candidate expression generation before model training, storing the results as configuration information. This pre-processing work ensures model completeness is achieved in advance, allowing the actual prediction process to use the pre-selected optimal feature set without time-consuming processing.
Solution Approach 2:
The system segments the comprehensive feature set into multiple candidate expressions with different complexities and characteristics. By dividing the feature space into manageable segments and evaluating them separately, the system identifies the most effective features efficiently, maintaining model completeness while reducing processing time.
Data Source
AI summary
Data processing techniques are provided. The techniques include: obtaining a first prediction data set, a model feature list and configuration information, wherein the model feature list indicates a plurality of features required by a data analysis model; generating a second prediction data set based on the model feature list and the first prediction data set, wherein the feature dimension of prediction data in the second prediction data set is smaller than the feature dimension of prediction data in the first prediction data set; performing feature transformation on a feature of the prediction data in the second prediction data set based on the configuration information to generate a third prediction data set; and inputting the third prediction data set into the data analysis model to obtain a prediction result.