Imputation Model for Incomplete Training Data in Reasoning Systems
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Solution Overview
Problem
Computer-based reasoning systems face challenges with incomplete training data, particularly when older systems lack sensors and data collection mechanisms used in newer systems, leading to sparse data that cannot be effectively used for model training.
Innovation Solution
The techniques involve analyzing cases in the computer-based reasoning model to determine missing fields, calculating conviction scores, and using these scores to prioritize and impute missing data through a supervised machine learning model, updating the model incrementally to ensure data completeness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If older systems are used for data collection, then historical data is available, but the data is incomplete due to lack of modern sensors and data collection mechanisms
Solution Approach 1:
The patent introduces an imputation model as an intermediary component that bridges the gap between incomplete historical data and the requirements of modern reasoning systems. This model predicts and fills in missing data fields using patterns learned from available data, allowing older systems' historical data to be effectively utilized without requiring physical modification of the original data collection infrastructure.
Solution Approach 2:
The patent replaces the mechanical approach of physically adding sensors to older systems with a computational approach. Instead of modifying the physical data collection mechanism, the system uses machine learning models to computationally infer and reconstruct missing data, substituting physical hardware modifications with software-based solutions.
2Loss of information
If data imputation is performed to complete sparse data, then data completeness is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent performs data imputation as a preliminary action before training the reasoning system. By completing the data set in advance using the imputation model, the system eliminates the need for complex real-time data handling during reasoning operations. This preliminary processing reduces the computational burden during actual system operation.
Solution Approach 2:
The patent segments the data processing task into distinct phases: first identifying missing fields, then applying the imputation model to fill them, and finally using the completed data for training. This segmentation allows each step to be optimized independently and enables parallel processing of multiple data records during the imputation phase.
3Adaptability or versatility
If retrofitted sensors are added to older oil pumps, then modern data can be collected, but the cost and complexity of system modification increase
Solution Approach 1:
The patent replaces physical sensor retrofitting with a computational imputation system. Instead of mechanically modifying older pumps by adding sensors and wiring, the system uses software-based data inference that works with the existing data collection infrastructure, thereby maintaining data compatibility without physical modification.
Solution Approach 2:
The imputation model serves as a universal solution that can handle various types of missing data from different sources and time periods. Rather than requiring specific sensor modifications for each older pump model, the computational approach universally addresses data completeness issues across diverse historical systems through pattern recognition and prediction.
Data Source
AI summary
Techniques are provided for imputation in computer-based reasoning systems. The techniques include performing the following until there are no more cases in a computer-based reasoning model with missing fields for which imputation is desired: determining which cases have fields to impute (e.g., missing fields) in the computer-based reasoning model and determining conviction scores and/or imputation order information for the cases that have fields to impute. The techniques proceed by determining for which cases to impute data and, for each of the determined one or more cases with missing fields to impute data is imputed for the missing field, and the case is modified with the imputed data. Control of a system is then caused using the updated computer-based reasoning model.