Weighted Feature Subset Prediction for Road Maintenance
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Solution Overview
Problem
Conventional regression analysis methods fail to accurately predict the maintenance control index (MCI) of road surfaces using features from moving images recorded by drive recorders, as they cannot effectively handle nonlinear and complex calculation rules.
Innovation Solution
An information processing apparatus and method that extracts subsets from training data, generates feature vectors, and trains a prediction model with weighted subsets to accurately predict the MCI, using a combination of data from drive recorders and dedicated measurement vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional regression analysis is used to predict MCI from road features, then the prediction process is simple, but the prediction accuracy is insufficient
Solution Approach 1:
The patent segments the road features into multiple subsets, where each subset captures different characteristics of the road condition. Instead of using all features uniformly, the method divides them into groups that can be independently weighted and combined, thereby improving prediction accuracy while managing complexity through structured organization of input data.
Solution Approach 2:
The patent applies different weights to different feature subsets based on their local importance to the MCI prediction. Each subset is evaluated for its specific contribution to predicting maintenance control index, allowing the model to focus computational resources on the most relevant features while ignoring less important ones, thus improving accuracy without linearly increasing overall complexity.
2Reliability
If all road features are used equally in prediction, then the model is simple to implement, but important local features may be overlooked
Solution Approach 1:
The patent transforms the uniform treatment of all features into a parameterized approach where each feature subset has an associated weight parameter. These parameters are learned during training to reflect the relative importance of different feature groups, enabling the model to adaptively emphasize critical local features while maintaining a systematic framework for feature processing.
3Measurement precision
If weighted feature subsets are used to improve prediction accuracy, then prediction precision increases, but computational complexity increases
Solution Approach 1:
By segmenting features into subsets, the computational task is divided into smaller, manageable pieces. Each subset can be processed independently with its own weight, reducing the computational burden compared to processing all features uniformly with equal complexity, while still achieving improved prediction accuracy through the weighted combination.
Data Source
AI summary
Various embodiments train a prediction model for predicting a label to be allocated to a prediction target explanatory variable set. In one embodiment, one or more sets of training data are acquired. Each of the one or more sets of training data includes at least one set of explanatory variables and a label allocated to the at least one explanatory variable set. A plurality of explanatory variable subsets is extracted from the at least one set of explanatory variables. A prediction model is trained utilizing the training data. The plurality of explanatory variable subsets is reflected on a label predicted by the prediction model to be allocated to a prediction target explanatory variable set with each of the plurality of explanatory variable subsets weighted respectively.