Directed Graph for Inter-Feature Dependencies in ML
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Solution Overview
Problem
Existing automated modeling systems assume feature independence, which leads to inaccurate simulations of output variations when modifying input features, as they fail to account for inter-feature dependencies, resulting in incomplete predictive modeling of user behavior and transactions.
Innovation Solution
The use of directed graph data structures that model inter-feature dependencies by representing input features as nodes and edges, where each edge indicates dependency, allowing for the propagation of changes in one input feature to dependent features, thereby updating their values based on edge weights and probability distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If feature independence is assumed in automated modeling systems, then the system complexity is reduced and processing is simplified, but the accuracy of predictive outputs deteriorates because inter-feature dependencies are not accounted for
Solution Approach 1:
The patent segments the feature space by identifying independent features and dependent features separately. Independent features are those not affected by other features, while dependent features are those influenced by others. This segmentation allows the system to handle complexity selectively - simplifying processing for independent features while applying dependency modeling only where necessary, thus resolving the contradiction between system complexity and prediction accuracy.
Solution Approach 2:
The patent introduces dynamic dependency modeling where the system adaptively determines which features have dependencies based on the specific dataset and problem context. Rather than assuming all features are independent or all are dependent, the system dynamically identifies and models only the necessary dependencies, allowing flexibility in balancing computational complexity with prediction accuracy for different scenarios.
2Ease of operation
If manual modification of input features is performed without considering dependencies, then the ease of operation is improved, but the reliability of simulation results worsens due to incomplete modeling of feature interactions
Solution Approach 1:
The patent implements feedback mechanisms where the system automatically detects and adjusts for dependency effects when users manually modify features. When a user modifies an independent feature, the system monitors and automatically updates dependent features to maintain realistic relationships, providing feedback that ensures simulation reliability without requiring the user to understand complex inter-feature dependencies.
Solution Approach 2:
The system performs self-service by automatically identifying and managing feature dependencies without requiring user intervention. When users modify input features, the system autonomously determines which other features should be adjusted based on learned dependencies, and automatically updates them accordingly. This allows users to operate the system easily while maintaining reliable simulation results through automated dependency management.
Data Source
AI summary
This disclosure involves generating graph data structures that model inter-feature dependencies for use with machine-learning models to predict end-user behavior. For example, a processing device receives an input dataset and a request to modify a first input feature of the input dataset. The processing device uses a graph data structure that models the inter-feature dependencies to modify the input dataset by propagating the modification of the first input feature to a second input feature dependent on the first input feature. The modification to the second input feature is a function of at least (a) the value of the first input feature and (b) a weight assigned to an edge linking the first input feature to the second input feature within the directed graph. The processing device then applies a trained machine-learning model to the modified input dataset to generate a prediction of an outcome.