Machine Learning Data Transformation for Autonomous Pattern Handling
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Solution Overview
Problem
Machine learning models face limitations in handling new input patterns, particularly in unstructured data, leading to potential exceptions and requiring repetitive human intervention for retraining, especially when dealing with repetitive patterns in high-frequency scenarios.
Innovation Solution
A method and system for data transformation using machine learning that generates a matrix for input vectors, compares intent values with a predefined threshold, determines a set of vectors with intent values below the threshold, and maps those vectors with intent values above the threshold to associated functions, enabling autonomous handling of new patterns without explicit retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional ML training techniques are used, then the model can handle known input patterns with high accuracy, but it requires repetitive human intervention for retraining when new patterns emerge
Solution Approach 1:
The system enables the ML model to automatically handle new input patterns through self-service mechanisms. When the model encounters unfamiliar patterns, it autonomously generates candidate transformations, evaluates them using a scoring function, and updates its own transformation rules without requiring human retraining intervention. This self-service capability allows the model to adapt to new patterns while maintaining high prediction accuracy for known patterns.
Solution Approach 2:
The transformation rules in the system are designed to be dynamic rather than static. The model continuously learns and updates its transformation rules based on new input patterns encountered during operation. This dynamic adaptation allows the system to evolve its capabilities over time, handling both known and new patterns effectively without requiring periodic human retraining.
2Reliability
If the ML model is retrained frequently to handle new patterns, then it can improve accuracy for new inputs, but it increases time loss and operational overhead
Solution Approach 1:
The system performs preliminary actions by pre-defining a schema-based framework and transformation rules that enable the model to handle new patterns incrementally. Instead of requiring complete retraining, the model uses pre-established rules and scoring functions to immediately begin processing new patterns, updating its knowledge base in real-time as new data is encountered.
Solution Approach 2:
The system maintains continuous useful action by enabling the ML model to operate continuously without interruption for retraining. The model processes new patterns in real-time, continuously updating its transformation rules while maintaining its operational workflow. This eliminates downtime associated with periodic retraining and ensures continuous productivity.
3Reliability
If human intervention is used to resolve new patterns, then the model can learn from expert knowledge, but it reduces productivity when new patterns occur at high frequency
Solution Approach 1:
The system implements self-service by enabling the ML model to autonomously resolve new patterns without human intervention. The model uses its scoring function to evaluate candidate transformations and automatically updates its rules, eliminating the need for human experts to review and approve each new pattern, thereby maintaining high processing speeds even when new patterns occur frequently.
Solution Approach 2:
The system incorporates feedback mechanisms where the model evaluates the performance of its transformations and uses this feedback to continuously improve. The scoring function provides quantitative feedback on transformation quality, allowing the model to learn from its own performance and automatically adjust its rules to maintain high prediction quality without human intervention.
Data Source
AI summary
A method and system for data transformation based on machine learning is disclosed. The method includes generating a matrix for a plurality of input vectors based on a machine learning model. The method further includes comparing for each of the plurality of input vectors, the intent value in the matrix with a predefined intent threshold, wherein, for an intent value below the predefined intent threshold, an associated function is unavailable. The method further includes determining a first set of vectors from the plurality of input vectors based on the comparing, wherein for each input vector in the first set, the associated intent value is below the predefined intent threshold. The method further includes mapping, by the data transformation device, each input vector in the first set with an intent value above the predefined intent threshold and an associated function.