Parallel Multiclass Classification Learning via Segmentation
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Solution Overview
Problem
Current large margin classifiers, such as SVMs, AdaBoost, and Maxent, face inefficiencies in learning speed when dealing with a large number of examples and classes, particularly in applications like natural language processing and network monitoring, where training times become excessive and memory requirements are high, especially when handling millions of examples and thousands of classes.
Innovation Solution
The proposed solution involves identifying shared processing steps among binary classifiers, preprocessing training data, and employing parallelization techniques that do not require communication between computing units, suitable for loosely connected networks, including data merging, transposing data representation, caching kernel products, and efficient load balancing and partitioning for parallel processing across multiple processors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large margin classifiers (SVMs, AdaBoost, Maxent) are used for multiclass classification with many classes, then classification accuracy is improved, but learning time increases excessively
Solution Approach 1:
The patent divides the multiclass classification problem into multiple binary classification problems. Instead of training one classifier to handle all classes simultaneously, the system creates multiple binary classifiers, each handling a subset of classes. This segmentation allows parallel processing and significantly reduces the learning time required for each individual binary classifier while maintaining overall classification accuracy through the combination of these binary classifiers.
2Adaptability or versatility
If the number of classes increases to handle diverse classification tasks, then model versatility is improved, but memory requirements and training time increase
Solution Approach 1:
The patent segments the classification task by dividing classes into groups and training separate binary classifiers for each group. This segmentation reduces the memory footprint of each individual binary classifier compared to a single multiclass classifier handling all classes, while the collection of binary classifiers provides comprehensive coverage for diverse classification tasks.
Solution Approach 2:
The patent creates a universal framework where multiple binary classifiers can be trained using the same algorithm and processing pipeline, but each classifier is specialized for a specific subset of classes. This multi-functionality allows the system to handle diverse classification tasks through a standardized approach, improving versatility without proportionally increasing memory requirements for each individual classifier.
3Productivity
If parallelization is implemented across multiple processors, then learning speed is improved, but system complexity and coordination overhead increase
Solution Approach 1:
The patent segments the classification problem into independent binary classification subproblems that can be distributed across multiple processors. Each processor trains a separate binary classifier for a specific subset of classes, eliminating the need for complex coordination and communication between processors. This segmentation-based parallelization improves learning speed while keeping system complexity manageable.
Solution Approach 2:
The patent implements a parallelization approach where each processor independently trains binary classifiers for its assigned subset of classes without requiring coordination or communication with other processors. Each processor serves itself by processing its own data and training its own classifiers independently, which improves learning speed while avoiding the complexity of inter-processor coordination and data sharing.
Data Source
AI summary
The time taken to learn a model from training examples is often unacceptable. For instance, training language understanding models with Adaboost or SVMs can take weeks or longer based on numerous training examples. Parallelization thought the use of multiple processors may improve learning speed. The invention describes effective methods to distributed multiclass classification learning on several processors. These methods are applicable to multiclass models where the training process may be split into training of independent binary classifiers.