Ensemble Classification Algorithms for Subclass Resolution
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Solution Overview
Problem
Binary decision trees used for classifying subjects into demographic sub-categories are prone to inaccuracies and require significant computational resources and time, making it difficult to achieve accurate subclass determinations and probabilistic predictions.
Innovation Solution
Ensemble classification algorithms leveraging fingerprint generation and distribution creation to predict subclass probabilities, which involve calculating primary subclass probabilities and creating probabilistic distributions for accurate and efficient subclass determinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If binary decision trees are used for classifying subjects into demographic sub-categories, then classification can be performed, but inaccuracies occur in separating and isolating characteristics leading to incorrect subclass determinations
Solution Approach 1:
The patent combines multiple classification algorithms into an ensemble system that integrates their predictions. Instead of relying on a single binary decision tree, the system merges results from multiple algorithms including decision trees, naive Bayes, and logistic regression, thereby improving subclass determination accuracy while maintaining reliability through diversified classification approaches
Solution Approach 2:
The patent introduces fingerprint vectors as intermediary representations that capture subject characteristics across multiple dimensions. These fingerprint vectors serve as mediators between raw input data and final classification decisions, enabling more accurate subclass determinations by representing subjects in a enriched feature space that preserves nuanced characteristics
2Productivity
If binary decision trees are used for classification, then subclass determinations can be made, but significant computational resources and time are required
Solution Approach 1:
The patent performs preliminary actions by pre-computing fingerprint vectors from training data and storing them for efficient retrieval during classification. The system pre-processes subject characteristics into compact fingerprint representations that can be quickly compared against test samples, significantly reducing computational resources and time required for actual classification operations
Solution Approach 2:
The patent creates simplified copies of complex classification logic through fingerprint-based representation. Instead of executing full decision tree algorithms during classification, the system uses pre-computed fingerprint vectors as compact proxies that enable rapid similarity-based classification, reducing computational overhead while maintaining classification quality
Data Source
AI summary
Ensemble classification algorithms having subclass resolution are disclosed. An example disclosed apparatus includes a fingerprint generator to generate a fingerprint of class probabilities of each of a plurality of samples, a distribution creator to create a distribution of the samples based on the generated fingerprints, and a distribution applicator to apply the distribution to a population to predict sub-class probabilities of each of the population.