Location Sensitive Ensemble Classifier for Content Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Ensemble machine learning classifiers often suffer from large data storage requirements and interpretability issues when composed of too many classifiers, leading to poor relevance in identifying low-quality content, which affects the usability of online services.
Innovation Solution
A location sensitive ensemble classifier is generated by dividing a validation data set into regions using locality sensitive hashing, and a regional ensemble classifier is created for each region, improving classification relevance and interpretability while reducing data storage needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an ensemble classifier is composed of many classifiers to improve classification accuracy, then classification performance is improved, but data storage requirements increase and interpretability deteriorates
Solution Approach 1:
The patent divides the validation dataset into multiple regions using locality sensitive hashing, and creates a separate regional ensemble classifier for each region. This segmentation allows each regional classifier to be smaller and more specialized, improving overall interpretability while maintaining accuracy through region-specific optimization.
Solution Approach 2:
The patent applies ensemble pruning locally within each region rather than globally across the entire dataset. This local quality approach allows each regional ensemble classifier to be optimized independently, reducing the number of classifiers needed in each region while maintaining overall classification accuracy.
2Reliability
If an ensemble classifier is composed of many classifiers to improve classification accuracy, then classification performance is improved, but data storage requirements increase
Solution Approach 1:
By segmenting the dataset into regions and creating separate regional ensemble classifiers, the patent reduces the total number of classifiers needed compared to a single global ensemble. Each regional classifier can be pruned more aggressively since it only needs to handle specific types of data points in its region.
Solution Approach 2:
The local ensemble pruning approach allows each regional classifier to be optimized for its specific region, removing redundant classifiers that would be necessary in a global ensemble but are unnecessary for region-specific classification tasks.
3Reliability
If an ensemble classifier is composed of many classifiers to improve classification accuracy, then classification performance is improved, but interpretability deteriorates
Solution Approach 1:
The patent segments the classification task into multiple regional classifiers, each handling a specific region of the feature space. This segmentation improves interpretability because each regional classifier can be understood and analyzed independently, rather than trying to interpret a single large global ensemble.
Solution Approach 2:
By applying pruning locally within each region, the patent creates smaller, more interpretable ensemble classifiers for each region. The reduced size of each regional ensemble makes it easier to understand which classifiers are important for that specific region, improving overall interpretability.
Data Source
AI summary
Computer-implemented systems and methods for generating and using a location sensitive ensemble classifier for classifying content includes dividing a validation data set into regions. Each region encompasses data points of the validation data set that fall within the region. A regional ensemble classifier is generated for each region based on the data points that fall within the region. A content item is then classified in at least one of a plurality of classes using the regional ensemble classifier for the region to which the content item belongs.