Classifier Construction via Data Clustering for Context Adaptation
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Solution Overview
Problem
Constructing a classifier that generalizes decision boundaries effectively across different contexts is challenging, as traditional methods rely on training data that may not account for varying conditions, leading to suboptimal performance when applied in new contexts.
Innovation Solution
The method involves clustering records to be classified, creating a primary classifier, and then generating specialized classifiers for each cluster, allowing the classifier to adapt to the specific context of the data set by using the records to be classified as a reference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a classifier is built using traditional training data methods, then the classifier can be constructed with standard training examples, but the classifier fails to generalize accurately across different contexts
Solution Approach 1:
The patent segments the data into multiple clusters based on contextual characteristics, then trains separate classifiers for each cluster. This allows the system to handle different contexts separately while maintaining overall classification capability, resolving the contradiction between accuracy and context generalization.
Solution Approach 2:
The patent creates a dynamic classifier system that can adapt to different contexts by selecting appropriate cluster-specific classifiers based on the input data's contextual characteristics. This dynamic adaptation enables the system to maintain high accuracy across varying contexts without requiring retraining.
2Reliability
If multiple classification techniques are used to ensure best classification results, then classification performance improves, but the complexity of the system increases
Solution Approach 1:
Instead of using multiple different classification techniques, the patent segments the data into clusters and trains multiple simpler classifiers on these segments. Each classifier is simple and specialized, but their combined performance through context-aware selection achieves high overall performance with reduced individual complexity.
Solution Approach 2:
The patent applies local quality by training specialized classifiers for specific clusters with distinct characteristics. Each classifier is optimized for its specific context rather than being a general-purpose model, achieving high performance through specialization rather than through multiple techniques.
3Ease of manufacture
If training data does not include contextual information, then the training process is simpler, but the classifier cannot account for varying conditions
Solution Approach 1:
The patent performs preliminary clustering of the training data into contextual groups before training the classifiers. This preliminary organization of data by context allows the classifiers to learn context-specific patterns without requiring complex contextual features in the training data itself, maintaining training simplicity while achieving contextual performance.
Solution Approach 2:
The patent uses cluster assignments as an intermediary that links training data to contextual information. The clustering process acts as a mediator that organizes training examples by implicit contextual characteristics, allowing the classifiers to capture contextual effects without direct contextual features in the training data.
Data Source
AI summary
A method and system for constructing a classifier for a set of records to be classified (108) into predicted classes (351-353) are provided. The set of records that are to be classified (108) are clustered into a plurality of clusters. A first classifier (106) is created that classifies records into the plurality of clusters (321-323) and the first classifier (106) is applied to a set of training records (110), each of the training records (331-334) having a predicted class (306). A classifier (107A-C) may then be created for each sub-set of training records (341-343) classed into each of the plurality of clusters and the classifier (107A-C) applied to a sub-set of records to be classified (311-313) formed in the corresponding cluster.