Balanced Train-Test Split Generation via Feature Clustering
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Solution Overview
Problem
Traditional train-test split evaluations are ineffective in environments with insufficient datasets to cover all common and uncommon cases, leading to imbalanced splits that fail to consider impactful features for machine learning models.
Innovation Solution
A method that automatically extracts low-level and high-level features, determines impactful features, selects subsets, clusters datasets, generates balanced train-test splits, scores each version, and selects the highest-scoring split to ensure that train-test splits are balanced in terms of both labels and features considered by the machine learning model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional train-test split evaluations are used, then the process is simple and fast, but the splits become imbalanced and fail to cover all common and uncommon cases when datasets are insufficient
Solution Approach 1:
The patent segments the dataset into multiple clusters based on feature similarity before creating train-test splits. This segmentation ensures that each cluster is represented in both training and test sets, maintaining balance and coverage of all cases including rare ones, thereby resolving the contradiction between reliability and simplicity.
Solution Approach 2:
The patent performs preliminary clustering of datasets based on impactful features before generating train-test splits. This preliminary action ensures that the splits are pre-balanced to cover all common and uncommon cases, improving reliability without requiring complex post-processing during evaluation.
2Reliability
If datasets are split without considering impactful features, then the split process is faster, but the resulting splits fail to balance labels and features for machine learning models
Solution Approach 1:
The patent extracts and identifies impactful features from the dataset that are most relevant to the machine learning model's predictions. By taking out only the most critical features for analysis rather than processing all features, the system achieves feature balance in splits while minimizing the time loss associated with feature extraction.
Solution Approach 2:
The patent changes the parameter of feature selection by focusing only on impactful features rather than all features. This parameter change allows the system to achieve balanced splits with respect to important features while reducing the computational time required for feature analysis.
3Reliability
If all features are considered in train-test split generation, then the splits are more balanced, but the computational complexity and processing time increase significantly
Solution Approach 1:
The patent extracts only the most impactful features that significantly influence the machine learning model's predictions. By selecting a subset of critical features rather than analyzing all features, the system maintains high balance quality in train-test splits while preserving processing speed and productivity.
Solution Approach 2:
The patent changes the parameter of feature consideration from all features to only impactful features. This parameter optimization allows the system to achieve high balance quality in splits without the computational overhead of processing every feature, thus maintaining productivity.
Data Source
AI summary
An embodiment for generating balanced train-test splits for machine learning analysis. The embodiment may automatically extract low-level features and high-level features from a series of received datasets. The embodiment may automatically determine a series of impactful features for each of the received datasets correlating to a corresponding label. The embodiment may automatically select subsets of impactful features The embodiment may automatically cluster the received datasets to generate series of clusters, each of the generated series of clusters corresponding to one of the selected subsets of impactful features. The embodiment may automatically generate train-test split versions using datasets from each cluster in each of the generated series of clusters. The embodiment may automatically score each of the generated train-test split versions and select a highest-scoring train-test split version.