Automatic Attribute Generation for AI Model Datasets
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Solution Overview
Problem
Existing machine learning models face inefficiencies due to the need for manual calculation and selection of attributes from large datasets, leading to inconsistencies, increased time, and resource wastage, as not all attributes are useful and can detriment the model's performance.
Innovation Solution
A method and system for automatically generating attributes through semantic categorization of large datasets, using an information pipeline to compute and transform data, and selecting optimal attributes based on hyperparameter settings, which can be used in machine learning models for classification, prediction, and data mining tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calculation and selection of attributes is performed, then attribute accuracy may be maintained, but time consumption and resource usage increase significantly
Solution Approach 1:
The system enables automatic attribute generation where the machine learning model itself identifies and creates relevant attributes from raw data without manual intervention. The model learns to select meaningful attributes autonomously during training, eliminating the need for manual attribute calculation while maintaining accuracy through automated relevance assessment.
Solution Approach 2:
The patent replaces manual mechanical processes of attribute selection with automated computational processes. Machine learning algorithms automatically generate and select attributes from large datasets, substituting human manual calculation with intelligent systems that can process and identify relevant features at scale without time constraints.
2Quantity of substance
If thousands of attributes are calculated and inserted into the model, then comprehensive data coverage is achieved, but model efficiency and speed decrease
Solution Approach 1:
The system extracts only the most relevant attributes from the dataset automatically. The machine learning model identifies and extracts meaningful features while discarding redundant or irrelevant attributes, achieving comprehensive data coverage with a focused subset of high-value attributes that maintain model speed and efficiency.
Solution Approach 2:
Rather than processing all possible attributes excessively, the system applies partial action by automatically selecting only the necessary portion of attributes needed for effective modeling. This selective approach avoids the performance degradation that would result from processing thousands of attributes while still capturing essential data patterns.
3Reliability
If manual attribute selection is performed, then control over attribute quality is maintained, but consistency and scalability are reduced
Solution Approach 1:
The automated attribute generation system provides universal functionality that works across different datasets, domains, and model types. The same automated process can be applied consistently to various data scenarios, maintaining reliability through standardized procedures while achieving scalability by eliminating manual intervention requirements.
Solution Approach 2:
The system automatically adjusts attribute generation parameters based on the specific dataset and modeling requirements. By dynamically changing parameters such as attribute selection criteria and transformation methods, the system maintains consistent quality control across different applications while adapting to various data characteristics and scalability needs.
Data Source
AI summary
Disclosed is a method and system to automatically generate attributes based on semantic categorization of large datasets in artificial intelligence models and/or applications. In one embodiment, a method of automatic representation of data includes organizing a substantial dataset in a manner through which it can serve as an input to a machine learning model and/or an artificial-intelligence application. The machine learning model is focused on classification, prediction, pattern search, trend search, data cluster search, data mining, and/or knowledge discovery. The method includes automatically creating a set of attributes for the machine learning model and/or the artificial-intelligence application that is usable on the substantial dataset. In addition, the method includes efficiently generating a data representation for the machine learning model and/or the artificial-intelligence application that is usable on the substantial dataset.