Image Representation of Categorical Data for Predictive Analysis
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Solution Overview
Problem
Existing predictive data analysis systems are inefficient and unreliable in handling high-dimensional categorical feature spaces with high cardinality, making it difficult to perform accurate and efficient predictive data analysis in complex domains.
Innovation Solution
The use of image transformations, such as reserved-spatial-location, coordinate-based, and feature-based transformations, to generate image representations of categorical input features, which are then processed by image-based machine learning models like CNNs for efficient and reliable predictive data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional predictive data analysis systems are used for high-dimensional categorical feature spaces, then the system structure is simple, but the processing efficiency is low and reliability is poor
Solution Approach 1:
The patent introduces an intermediary transformation process that converts categorical feature data into image representations. This intermediary step enables the use of efficient image-based machine learning models while maintaining compatibility with categorical data inputs, thereby improving processing efficiency without requiring fundamental changes to the overall system architecture.
Solution Approach 2:
The patent transforms the representation parameters of categorical data by converting them into image-based parameters. This parameter transformation allows the data to be processed by image-based machine learning models which have higher processing efficiency and reliability for this type of data structure.
2Reliability
If traditional methods are used for high-cardinality categorical features, then the approach is straightforward, but the prediction reliability is low
Solution Approach 1:
The image transformation process serves as an intermediary that bridges categorical features and prediction models. This transformation improves prediction reliability by converting categorical data into a format that preserves more information and relationships, enabling image-based models to achieve better predictive performance on high-cardinality features.
3Measurement precision
If categorical feature data is processed directly without transformation, then the data processing pipeline is simple, but the analysis accuracy is insufficient for complex domains
Solution Approach 1:
The patent applies parameter changes by transforming categorical feature representations into image-based representations. This transformation enhances analysis accuracy for complex domains by preserving more structural information and relationships in the data, allowing image-based machine learning models to achieve superior predictive performance.
Data Source
AI summary
There is a need for more effective and efficient predictive data analysis solutions and/or more effective and efficient solutions for generating image representations of categorical/scalar data. Various embodiments of the present invention address one or more of the noted technical challenges. In one example, a method comprises receiving the one or more categorical input features; generating an image representation of the one or more categorical input features, wherein the image representation comprises image region values each associated with a categorical input feature, and further wherein each image region value of the one or more image region values is determined based at least in part on the corresponding categorical input feature associated with the image region value; and processing the image representation using an image-based machine learning model to generate the image-based predictions.