Continuous Feature Binning for Machine Learning Model Stability
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Solution Overview
Problem
Existing feature processing methods for machine learning struggle to achieve a satisfactory effect due to instability in model predictions caused by inappropriate histogram division and dependence on bin selection, leading to increased workload in parameter adjustment.
Innovation Solution
A feature processing method that performs a basic binning operation and additional operations on continuous features to generate multiple features, improving the effectiveness of machine learning samples by characterizing data from different aspects and scales.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single histogram division method is adopted for feature extraction, then the processing method is simple, but the machine learning model becomes unstable and prediction效果 varies significantly under different binning modes
Solution Approach 1:
The patent divides the continuous feature into multiple bins using different binning methods (equal-width binning, equal-frequency binning, and custom binning) to generate multiple discrete features. This segmentation approach allows the model to capture different aspects of the data distribution, improving stability while maintaining reasonable processing complexity.
Solution Approach 2:
The patent transforms a single continuous feature into multiple discrete features by applying different binning strategies, effectively adding dimensionality to the feature representation. This multi-dimensional feature extraction ensures that the model can learn from various perspectives of the same underlying data, reducing prediction instability.
2Ease of manufacture
If traditional binning method is used for feature extraction, then the processing is straightforward, but the model requires extensive parameter adjustment and the learning effect is highly dependent on bin selection
Solution Approach 1:
The patent implements a universal feature extraction framework that simultaneously applies multiple binning methods (equal-width, equal-frequency, and custom binning) to generate comprehensive feature sets. This multi-functional approach reduces the need for extensive parameter tuning by providing diverse feature representations that are more robust to hyperparameter variations.
Solution Approach 2:
The patent changes the parameters of binning operations by using different binning strategies with varying characteristics. Instead of manually adjusting model hyperparameters extensively, the system varies the binning parameters to generate diverse features, which indirectly optimizes model performance with less manual intervention.
3Device complexity
If continuous features are directly used without additional operations, then the feature extraction is simple, but the model cannot effectively capture data characteristics from different aspects and scales
Solution Approach 1:
The patent segments continuous features into discrete bins using multiple binning methods, transforming a single continuous value into multiple discrete feature indicators. This segmentation preserves different aspects of the data characteristics (distribution, scale, patterns) that would be lost if continuous features were used directly, while maintaining manageable processing complexity.
Data Source
AI summary
Provided are a feature processing method and feature processing system for machine learning. The feature processing method includes: (A) acquiring a data record, wherein the data record comprises at least one piece of attribute information; (B) for each of the continuous features generated based on at least a some of the attribute information in the at least one piece of attribute information, executing a basic binning operation and at least one additional operation to generate a basic binning feature and at least one additional feature corresponding to each of the continuous features; and (C) generating a machine learning sample at least comprising the generated basic binning feature and at least one additional feature. By means of the above-mentioned method, a plurality of features comprising the binning feature and corresponding to the continuous features can be acquired, so as to make the features constituting the machine learning sample more effective, so as to improve the effect of a machine learning model.