Category Proportion Calculation Using Content Profiles
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Solution Overview
Problem
Conventional methods for determining the distribution of content across categories in large datasets are prone to biases and become increasingly challenging with dataset size and complexity, making accurate classification of unstructured and structured data infeasible.
Innovation Solution
The system employs a computer-implemented method using stacked regression, bias formula, and noise elimination methods, or an ensemble of algorithms to calculate category proportions in a population set by analyzing content profiles from a training set and applying them to a population set, allowing for accurate measurement of category distributions without individual post classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional classification techniques are applied to large datasets, then individual element classification accuracy can be improved, but aggregate proportion measurement becomes biased and increasingly difficult with dataset size and complexity
Solution Approach 1:
The patent segments the classification problem into two distinct stages: (1) training phase where individual elements are classified to create content profiles, and (2) estimation phase where aggregate proportions are calculated from these profiles using statistical methods. This segmentation allows individual classification accuracy to be optimized independently from aggregate proportion measurement, resolving the contradiction between the two requirements.
Solution Approach 2:
The patent introduces content profiles as an intermediary representation between individual classified elements and aggregate proportion estimates. These profiles capture the essential characteristics of classified elements and serve as the basis for statistical estimation of population proportions, thereby mediating between individual-level accuracy and aggregate-level reliability.
2Loss of information
If individual classification of elements is performed on a large scale, then detailed category distribution information can be obtained, but computational infeasibility and time consumption increase significantly
Solution Approach 1:
The patent performs preliminary classification of a training set of elements to create content profiles before analyzing the larger population set. This preliminary action captures the essential classification information needed for aggregate proportion estimation, allowing the main analysis to proceed much faster without requiring complete individual classification of all population elements.
Solution Approach 2:
The patent creates content profiles that copy or summarize the essential characteristics of classified elements. These profiles serve as compressed representations that retain the necessary information for proportion estimation while dramatically reducing the data volume and processing time required compared to working with individual classified elements.
3Ease of manufacture
If traditional algorithms are used for category proportion calculation, then implementation simplicity is maintained, but accuracy degrades with imbalanced data and small training sets
Solution Approach 1:
The patent changes the key parameter from individual classification labels to content profiles that summarize element characteristics. This parameter transformation enables the use of statistical estimation methods that work effectively with small training sets and imbalanced data, improving accuracy while maintaining reasonable implementation complexity through standardized statistical techniques.
Data Source
AI summary
Systems and methods are provided for classifying text based on language using one or more computer servers and storage devices. A computer-implemented method includes receiving a training set of elements, each element in the training set being assigned to one of a plurality of categories and having one of a plurality of content profiles associated therewith; receiving a population set of elements, each element in the population set having one of the plurality of content profiles associated therewith; and calculating using at least one of a stacked regression algorithm, a bias formula algorithm, a noise elimination algorithm, and an ensemble method consisting of a plurality of algorithmic methods the results of which are averaged, based on the content profiles associated with and the categories assigned to elements in the training set and the content profiles associated with the elements of the population set, a distribution of elements of the population set over the categories.