Multi-Stage Learning System for ML Cold Start Classification
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Solution Overview
Problem
Machine learning models face challenges with cold starts and misclassification of rare inputs due to limited historic data and difficulties in assigning weights to fresh training data based on user feedback, especially in enterprise-specific applications.
Innovation Solution
A multi-stage continuous learning/feedback system utilizing a fuzzy dictionary in combination with a machine learning model, where terms are classified based on effective counts with a time decay factor and fuzzy matching, and a multilevel dictionary prioritizes user-level preferences over organizational levels, allowing for efficient classification and adaptation to user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning models are deployed with limited historic data, then personalized models can be created for individual users, but cold start problems occur during which models misclassify inputs at unacceptable levels
Solution Approach 1:
The system pre-populates the dictionary with historical classification data and effective counts before the machine learning model begins operation. This preliminary action ensures that the dictionary contains sufficient information to handle cold start scenarios, allowing accurate classifications even when the ML model has not yet accumulated enough user-specific data.
Solution Approach 2:
The dictionary serves as an intermediary between the limited historic data and the machine learning model. It provides a structured repository of term classifications and effective counts that the ML model can reference during cold start periods, bridging the gap when direct user interaction data is insufficient.
2Adaptability or versatility
If machine learning models are used for classification, then they can handle complex patterns, but they misclassify rarer inputs (minority classes) which either do not have meaningful training data or are relatively anomalous
Solution Approach 1:
The dictionary is pre-populated with classification data for minority classes and rare terms from historical data before the ML model operates. This ensures that even terms with limited training data have established classifications and effective counts in the dictionary, preventing misclassification during cold start and early operation phases.
Solution Approach 2:
The system uses effective counts that incorporate time decay factors to dynamically adjust the weight of historical classifications. This parameter change allows the system to balance between established dictionary classifications and emerging ML model predictions, particularly benefiting minority classes by giving appropriate weight to historical classification patterns.
3Reliability
If user feedback is used to train machine learning models, then model accuracy can improve over time, but difficulties arise when assigning weights to fresh training data
Solution Approach 1:
The system implements a feedback mechanism where user classifications are continuously incorporated into the dictionary, updating effective counts for terms and classes. This feedback loop allows the system to adapt to changing user preferences and terminology while automatically managing data weighting through the time decay factor in effective count calculations.
Solution Approach 2:
The effective count parameter incorporates a time decay factor that automatically adjusts the weight of training data based on its recency. This parameter change simplifies the weight assignment process by providing a systematic approach to balancing fresh training data against historical classifications, reducing the complexity of manual weight tuning.
4Adaptability or versatility
If personalized machine learning models are deployed per user, then individual user needs can be met, but resource requirements and operations and maintenance challenges increase
Solution Approach 1:
The system merges the benefits of personalized ML models with a shared organizational dictionary. The dictionary serves as a common resource that all users benefit from, containing aggregated classification knowledge and effective counts. This combination reduces resource requirements compared to fully personalized models while maintaining user-specific adaptation capabilities through the ML component.
Solution Approach 2:
The dictionary is designed as a universal resource that serves multiple functions: providing cold start classifications, supporting minority class recognition, enabling continuous learning through feedback, and reducing computational overhead. This multi-functionality allows a single dictionary structure to address multiple challenges that would otherwise require separate personalized solutions for each user.
Data Source
AI summary
Data is received that specifies a term generated by user input in a graphical user interface. Thereafter, the term is looked up in a dictionary in which there are multiple classes for terms. The term can be classified based on a first class having a top ranked effective count for the term within the dictionary when a ratio of the first class relative to a second class having a second ranked effective count for the term in the dictionary is above a pre-defined threshold. In addition, the term is classified using a machine learning model when the ratio of the first class relative to the second class is below the pre-defined threshold. Data can be provided which characterizes the classifying. Related apparatus, systems, techniques and articles are also described.