Inductive Learning for Multiclass Labeling With Less Manual Training Data
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Solution Overview
Problem
Supervised learning models require large volumes of labeled data for accurate multiclass classification, which is tedious and time-consuming, and are prone to class imbalance issues leading to inaccurate predictions.
Innovation Solution
A machine-learning model training system that selects intelligently labeled multiclass vectors using ground truth and boundary condition vectors, employing inductive learning and forgetting to improve model performance without extensive manual labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised learning models are trained with large volumes of labeled data, then prediction accuracy is improved, but training time and manual labeling effort increase
Solution Approach 1:
The system performs preliminary clustering analysis on unlabeled data to identify ground truth vectors and boundary condition vectors before formal model training. This preliminary organization of data into meaningful groups allows the model to learn from structured representations rather than raw unlabeled data, reducing the amount of manual labeling required while maintaining prediction accuracy
Solution Approach 2:
The patent introduces clustering algorithms as an intermediary mechanism between raw feature vectors and the supervised learning model. This intermediary process automatically generates pseudo-labels and identifies critical vectors (ground truth and boundary conditions) that bridge the gap between unlabeled and labeled data, eliminating the need for extensive manual labeling while preserving model accuracy
2Measurement precision
If supervised learning models use more labeled data for multiclass classification, then model performance is improved, but class imbalance issues worsen
Solution Approach 1:
The system applies different treatment to different regions of the feature space by identifying ground truth vectors (representing clear class centers) and boundary condition vectors (representing class boundaries). This local differentiation allows the model to focus learning efforts on critical regions while automatically handling class imbalance, as boundary vectors from minority classes receive appropriate attention regardless of their frequency in the dataset
3Measurement precision
If manual labeling of large datasets is performed, then data quality is improved, but productivity decreases
Solution Approach 1:
The system enables data to label itself through automated clustering analysis. Unlabeled feature vectors are processed by clustering algorithms that automatically assign pseudo-labels based on their proximity to ground truth vectors and their relationship to boundary condition vectors. This self-labeling mechanism eliminates manual intervention while maintaining data quality, dramatically improving training efficiency
Solution Approach 2:
The patent creates copies of ground truth vectors and uses them to generate pseudo-labels for unlabeled data through clustering. Instead of manually creating labels for each data point, the system replicates the labeling pattern from known ground truth vectors to similar unlabeled vectors based on their spatial relationships in the feature space, maintaining consistency while scaling efficiently
Data Source
AI summary
Systems and methods are described for training a machine learning model using intelligently selected multiclass vectors. According to an embodiment, an un-labeled feature vector is selected from a set of feature vectors. A model classified cluster and a confidence score are obtained by classifying an un-labeled feature vector using a machine-learning model. A determination is made regarding whether the confidence score is greater than a threshold. When the determination is affirmative: (i) for each labeled feature vector, determining a distance metric for the un-labeled feature vector with respect to the labeled feature; (ii) determining a statistically matching cluster of labeled feature vectors to which the un-labeled feature vector is closest; and (iii) when the model classified cluster and the statistically matching cluster are one and the same: (a) labeling the un-labeled feature vector; and (b) model fitting the machine learning model based on the labeling.