Data Processing Apparatus for Feature Summarization
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Solution Overview
Problem
In systems where analysis data from multiple analyzers is processed, users face challenges in summarizing features due to variability and outliers, requiring extensive time and effort to confirm distribution states for appropriate feature selection in statistical or AI analysis.
Innovation Solution
A data processing method and apparatus that collect analysis data from various analyzers, select relevant data sets, extract features, perform statistical processing to summarize features, and present statistical information to users, enabling efficient feature summarization and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If averaging processing is performed to summarize multiple features, then feature summarization is achieved, but the presence of outliers causes inappropriate summarization
Solution Approach 1:
The system performs preliminary visualization of feature distributions before summarization, allowing users to identify outliers and inappropriate data points beforehand. This preliminary action enables users to make informed decisions about data preprocessing, such as excluding outliers or transforming distributions, before applying summarization methods like averaging, thereby ensuring both efficiency and accuracy.
2Measurement precision
If users manually confirm the distribution state of features, then appropriate feature selection is achieved, but time and effort requirements increase significantly
Solution Approach 1:
The system introduces an automated intermediary component that generates visual representations of feature distributions (such as histograms, box plots, or density plots) and provides automated statistical analyses. This intermediary enables users to quickly assess feature distributions and identify outliers without manual inspection, significantly reducing the time and effort required while maintaining accurate feature selection.
3Quantity of substance
If multiple features from multiple analyzers are collected, then comprehensive analysis data is obtained, but the variability and complexity of feature types increase
Solution Approach 1:
The system implements a universal feature processing framework that can handle multiple types of features from different analyzers through standardized procedures. The framework includes automated feature type detection, appropriate visualization method selection, and adaptive summarization techniques that work across diverse feature types, thereby managing complexity while maintaining comprehensive data analysis capabilities.
Data Source
AI summary
A data processing method according to one aspect of the present invention includes: a step of collecting an analysis file set including analysis data by an analyzer from a plurality of types of analyzers; a step of selecting a plurality of analysis file sets to be analyzed from the plurality of types of collected analysis file sets according to a narrowing-down condition received from a user; a step of extracting a plurality of features from each of the plurality of selected analysis file sets; a step of summarizing the plurality of features by performing statistical processing of the plurality of features; and a step of performing an analysis using the summarized features. The step of summarizing the plurality of features includes a step of presenting statistical information generated in the statistical processing to the user.