Classifier Learning System for Variable Feature Spaces
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Solution Overview
Problem
Existing classifier learning systems struggle to efficiently operate in environments with variable feature spaces, where sensors are added or removed, and data collection environments change, making it difficult to combine data sets with different features and handle missing values effectively.
Innovation Solution
A classifier learning system that includes a feature weight generation module using artificial neural networks and mutual information to assign weights to features, and a data sampling module that performs feature space restoration operations to generate sampling data, allowing the classifier to adapt to new and combined feature spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a classifier is trained on training data with a fixed feature space, then the classification algorithm can operate efficiently on that specific feature space, but it cannot adapt to new feature spaces where sensors are added or removed or data collection environments change
Solution Approach 1:
The patent implements dynamic feature space handling by allowing the classifier to adapt its feature space over time through progressive learning. The system dynamically adds or removes features based on incoming data, enabling the classifier to adjust to changing environments while maintaining stable performance through controlled learning processes.
Solution Approach 2:
The patent changes the parameter of feature space configuration from fixed to variable. By allowing features to be added or removed dynamically and using techniques like feature weighting and selection, the system adapts to different data collection environments while maintaining classification reliability through parameter optimization.
2Productivity
If data sets with different features are combined using expert selection or algorithmic filling of missing values, then the feature space can be unified, but this method is unsuitable for processing large amounts of missing values that continuously come in
Solution Approach 1:
The patent implements self-service through automated feature selection and weighting mechanisms that do not require expert intervention. The system automatically handles missing values and feature alignment through algorithmic processes, enabling efficient processing of large amounts of continuously incoming data with varying features.
Solution Approach 2:
The patent transforms the complex problem of feature space unification into a parameter optimization problem. By using feature weighting parameters and selection criteria that can be automatically adjusted, the system simplifies the handling of variable feature spaces while maintaining processing efficiency for large datasets.
3Loss of information
If conventional progressive learning algorithms are used, then cumulative performance on streaming data can be improved, but they do not consider performance from a forgetting or aggregation perspective of knowledge
Solution Approach 1:
The patent implements feedback mechanisms where the classifier continuously evaluates its performance on both new and previously learned feature spaces. This feedback loop enables the system to detect knowledge forgetting and trigger relearning or aggregation processes, ensuring knowledge retention while maintaining learning efficiency through targeted updates.
Solution Approach 2:
The patent performs preliminary actions by pre-processing and storing feature space information in advance. This includes maintaining records of previously learned feature spaces and their characteristics, which allows the system to efficiently aggregate knowledge when new feature spaces are encountered, reducing the need for complete relearning.
Data Source
AI summary
Disclosed is a classifier learning system, which includes a classifier that trains training data having a feature space including a plurality of features based on a classification algorithm, a feature weight generation module that generates a feature weight based on an artificial neural network and an amount of mutual information between the plurality of features of the training data, and a data sampling module that generates sampling data by performing a feature space restoration operation based on the training data and a previous feature space of previous data on which the training is completed in the classifier, and the classifier trains the sampling data, and the classifier includes a plurality of feature-specific classifiers to which the feature weights corresponding to each of the plurality of features are assigned.