Prediction Evaluation Using Complementary Data and Perturbation Analysis
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Solution Overview
Problem
Existing technologies face difficulties in evaluating change attributes and amounts when a portion of attribution values in input data has defects, making it challenging to suggest effective actions for achieving desired prediction results.
Innovation Solution
An evaluation program generates complementary data patterns to address defects, determines perturbation information for changing labels, and evaluates this information based on determination results to suggest effective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complementary data is generated to handle defective input data, then the ability to evaluate change attributes and amounts is improved, but the complexity of the processing system increases
Solution Approach 1:
The patent segments the processing of defective data by generating multiple complementary data patterns separately, evaluating perturbation information for each pattern independently, and then synthesizing the results. This allows the complex task of handling defective data to be broken down into manageable steps that can be processed systematically.
Solution Approach 2:
The patent performs preliminary actions by generating complementary data patterns before the actual evaluation of change attributes. By pre-processing the defective data into multiple valid patterns, the system prepares the data in advance, making the subsequent evaluation process more accurate and manageable.
2Reliability
If multiple complementary data patterns are generated and evaluated, then the reliability of action suggestions is improved, but the processing time increases
Solution Approach 1:
The patent applies partial action by generating a limited number of complementary data patterns rather than exhaustively processing all possible patterns. This allows the system to achieve sufficient reliability for action suggestions while avoiding excessive processing time that would result from evaluating every possible data variation.
Solution Approach 2:
The system performs self-service by automatically generating complementary data patterns and evaluating perturbation information without requiring external intervention. This automated process improves reliability through consistent application of evaluation criteria while minimizing the time loss associated with manual processing.
3Measurement precision
If perturbation information is determined from multiple patterns, then the accuracy of predicted label changes is improved, but the computational resources required increase
Solution Approach 1:
The patent merges the evaluation results from multiple complementary data patterns to determine the final perturbation information. By combining the findings from different patterns, the system achieves more accurate predictions of label changes while optimizing computational resource usage through shared processing components and methodologies.
Data Source
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AI summary
An evaluation program causing a computer to execute a process. The process includes: generating, when a portion of values of a plurality of attributes included in input data has a defect, complementary data of a plurality of patterns obtained by complementing the defect in a plurality of ways; determining perturbation information including an attribute to be changed and a change amount from among the plurality of attributes of complementary data in order to change a label predicted by the complementary data of the plurality of patterns; and evaluating the perturbation information based on a determination result as to whether the perturbation information determined in the complementary data of one pattern among the plurality of patterns can change the label also with respect to the complementary data of another pattern among the plurality of patterns.