Synthetic Data Generator for ML Classification Accuracy
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Solution Overview
Problem
Conventional machine learning classifiers face inaccuracies and decreased predictive efficiency due to deficiencies in training sets, particularly when large, high-quality datasets are unavailable or clustered and anonymized, leading to issues like false positives and false negatives.
Innovation Solution
The development of advanced techniques for generating accurate training sets through data mining and transformation, utilizing multiple data sources to supplement and improve the quality of data objects, and employing ensemble classifiers to enhance prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large training sets are used to improve classification accuracy, then predictive efficiency is improved, but data availability and quality become problematic due to clustering and anonymization requirements
Solution Approach 1:
The patent creates synthetic copies of real data objects through the data generator, which replicates data patterns without using actual sensitive information. This allows the system to generate unlimited training data that maintains statistical properties of real data while avoiding privacy issues associated with using actual clustered and anonymized data sets.
Solution Approach 2:
The patent introduces a data generator as an intermediary component that transforms real data into synthetic data. This intermediary layer enables the system to access training data without directly handling sensitive real-world data, thus resolving the contradiction between needing large training sets and avoiding privacy problems with clustered/anonymized data.
2Ease of manufacture
If conventional classifiers are used for classification tasks, then implementation is simple, but false positives and false negatives increase due to training set deficiencies
Solution Approach 1:
The patent implements a feedback mechanism where the data generator uses information about false positives and false negatives from the classifier to adjust and refine synthetic data generation. This iterative feedback loop continuously improves the quality of training data, thereby reducing prediction errors while maintaining system reliability.
Solution Approach 2:
The system performs self-service by automatically generating and refining its own training data without requiring external intervention. The data generator self-adjusts based on classifier performance metrics, enabling the system to improve its own training quality and reduce false positives/negatives autonomously.
Data Source
AI summary
A machine learning and predictive analytics system is disclosed. The system may comprise a data access interface to receive, over a network, data associated with a subject from a data source. The data source may include an internal data source and an external data source. The system may comprise at least one processor to analyze the data associated with the subject, predict a future life event based on the analysis of the data, and calculate at least one of a financial forecast, a ratio, and an index based on the predicted future life event and data associated with the subject. The processor may use machine learning, statistical analysis, simulation, and/or modeling techniques to analyze the data, predict the future life event, and calculate the at least one of a financial forecast, a ratio, and an index, which may represent likelihood of the subject taking a financial action with a financial institution. The processor may also generate a recommendation for the subject to elect the financial action or other product or service based on the predicted life event.