Class-Specific Data Transformation for Predictive Model Accuracy
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Solution Overview
Problem
Existing data science techniques treat input data as invariant during downstream processing, failing to effectively utilize class association functions to enhance predictive model outputs, leading to suboptimal performance and accuracy.
Innovation Solution
A method that involves deriving class association function characteristic data to construct a class-specific transformation function, which improves input data by associating entities with classes, thereby enhancing predictive model outputs through data transformation using a computer processor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If class association functions are treated as independent of downstream data science processing, then the system maintains simplicity in data processing, but predictive model accuracy deteriorates due to failure to utilize class information for data transformation
Solution Approach 1:
The patent segments the data processing pipeline by introducing class-specific transformation functions that operate independently for each class of entities. This allows the system to apply targeted transformations based on entity classes while maintaining overall system modularity. The segmentation enables accurate utilization of class information without requiring complete redesign of the entire processing system.
Solution Approach 2:
The patent applies preliminary actions by performing class-specific data transformations before the main predictive modeling stage. The class association function characteristic data is derived and transformation functions are constructed in advance, allowing the input data to be pre-processed and optimized for each class before being fed into the predictive model, thereby improving accuracy without adding complexity to the core modeling process.
2Reliability
If class-specific transformation functions are constructed using class association function characteristic data, then input data quality improves, but processing time and computational effort increase
Solution Approach 1:
The patent applies partial action by selectively transforming only the portions of input data that benefit from class-specific processing. Rather than transforming all data uniformly, the system identifies and transforms only those features and entities where class association information provides value, reducing unnecessary computational overhead while maintaining data quality where it matters most.
Solution Approach 2:
The patent changes parameters by dynamically adjusting transformation parameters based on class association function characteristic data. The transformation functions modify data parameters such as feature scaling, sampling rates, and feature selection criteria according to the specific class being processed, optimizing the balance between data quality improvement and processing efficiency for each class.
3Measurement precision
If existing techniques treat input data as invariant during downstream processing, then processing simplicity is maintained, but predictive output quality deteriorates due to inability to adapt data to model requirements
Solution Approach 1:
The patent introduces dynamics by making the data transformation process adaptive rather than static. The class-specific transformation functions dynamically adjust data characteristics based on the entity class and the requirements of the predictive model. This allows the input data to evolve and adapt during processing, improving predictive output quality while maintaining manageable complexity through class-based organization.
Data Source
AI summary
The quality of predictive model outputs is improved by improving input data in the cases where entities in the input data are associated with one or more classes, computed at least in part from one or more subsets of the input data. Class association function characteristic data is derived from information describing a class association function that generates input data from source data. The class association function characteristic data comprises inferences relating to operation of the class association function that are not derivable solely from the input data. The input data is transformed into improved input data using a constructed class-specific transformation function and the class association function characteristic data.