Tree Model Interaction Score Calculation for Feature Association
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Solution Overview
Problem
Current methods for analyzing the association between gut microbiota and disease, such as those using traditional statistical methods, face challenges in handling multiple factors and interactions, and fail to extract important factors in predicting pharmacological phenotypes effectively.
Innovation Solution
An arithmetic device constructs a classification and prediction model with a tree structure using feature amount vectors and event data, calculating an interaction score based on the position of feature amounts within the model and their shuffled positions to score the association between feature amounts and events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional statistical methods are used for association analysis in gut microbiota studies, then the analysis can be performed with established techniques, but multiple testing problems arise when dealing with a large number of factors
Solution Approach 1:
The patent transforms the analytical approach by changing from traditional statistical parameter testing to a machine learning model-based interaction scoring system. The model constructs feature amount vectors and calculates interaction scores between factors, avoiding multiple testing issues while maintaining analysis reliability through a unified scoring framework.
2Productivity
If machine learning methods are used to analyze a large number of factors and interactions, then the analysis capability is improved, but it becomes difficult to extract important factors from the model
Solution Approach 1:
The patent extracts important factors from the machine learning model by calculating interaction scores that quantify the degree of association between feature amounts and events. The system outputs these scores to explicitly identify which factors and their interactions are most important, preventing information loss while maintaining high analysis capability.
3Measurement precision
If a classification and prediction model with tree structure is constructed using feature amount vectors, then the model can classify and predict events effectively, but the complexity of calculating interaction scores based on node positions increases
Solution Approach 1:
The patent employs a self-service approach where the tree structure model automatically provides the interaction score calculation framework. The position of feature amounts in the tree nodes directly determines their interaction scores, allowing the model structure itself to serve the dual purpose of prediction and interaction quantification without requiring separate complex calculation mechanisms.
Data Source
AI summary
System and method for calculating interaction between feature amounts, including a model construction unit for acquiring data including a feature amount vector which is a set of numerical values of feature amounts as an explanatory variable, and information of an event as an objective variable, and constructing a classification and prediction model having a tree structure for classifying and predicting the event based on the feature amount vector, an interaction score calculation unit for calculating an interaction score indicating a degree of association of interaction between the feature amounts with the event is based on a position of the feature amount appearing in a node constituting the classification and prediction model, and a position of the feature amount in the classification and prediction model in which the position of the feature amount appearing in the node has been shuffled, and an output processing unit for outputting the calculated interaction score.