Siamese Triplet LSTM for Imbalanced Data Validation
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Solution Overview
Problem
Existing data field validation methods, particularly in computer databases and file management, face challenges with inconsistencies and data quality issues due to extreme class imbalance in training data sets, limiting their effectiveness in automated analysis.
Innovation Solution
A machine learning-based data field validation system utilizing a Siamese triplet LSTM network architecture, which includes three LSTMs trained together for positive, anchor, and negative class embeddings, is proposed to address class imbalance and enhance data quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static rules based approaches are used for data field validation, then the system is simple to implement, but it is limited to specific use cases and cannot handle extreme class imbalance in training data
Solution Approach 1:
The system segments the validation problem into multiple specialized LSTM networks (first LSTM for positive class, second LSTM for anchor class, third LSTM for negative class), each trained on specific data subsets. This segmentation allows the system to handle different use cases effectively while managing complexity through modular architecture.
Solution Approach 2:
The triplet LSTM network architecture provides a universal solution that handles both balanced and extremely imbalanced datasets through the same fundamental mechanism. The system can be applied to various data validation scenarios (single-country vs multi-country address validation) without requiring fundamental redesign, achieving versatility across different use cases.
2Reliability
If traditional machine learning models are used, then training data requirements are standard, but they fail to effectively handle extreme class imbalance with very few positive examples
Solution Approach 1:
Each LSTM network in the triplet architecture is specialized to process specific class relationships (positive, anchor, negative). The first LSTM focuses on positive class embeddings, the second on anchor class, and the third on negative class. This local specialization allows reliable validation even when overall training data is imbalanced, as each component is optimized for its specific function.
Solution Approach 2:
The anchor class embeddings serve as an intermediary that bridges the gap between positive and negative examples. By introducing this intermediate representation, the system can learn meaningful patterns even when direct positive-negative pairs are scarce, effectively mediating the learning process in imbalanced scenarios.
3Quantity of substance
If Siamese networks are used to handle class imbalance, then the system can work with fewer training examples, but a large amount of data is still required to establish sufficient training examples
Solution Approach 1:
The system merges three separate LSTM networks into a unified triplet architecture where all three LSTMs are trained together and operate in concert. This combining of multiple specialized networks allows the system to effectively utilize limited training examples by leveraging the complementary information from each network while managing complexity through integrated training.
Data Source
AI summary
A system and method for machine learning-based data field validation is proposed that utilizes a specific trained machine learning model data architecture that is adapted to be more resilient against training set class imbalance, using a Siamese triplet LSTM network architecture that uses three LSTMs that are trained together and operate in concert. An example non-limiting practical use includes using the Siamese triplet LSTM network architecture to validate whether free-text data fields include a single jurisdiction in an address or multiple jurisdictions in the address.