Target Domain Classifier Training via Sequential Label Prediction
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Solution Overview
Problem
Conventional machine learning-based automation systems for domain adaptation and transfer learning assume upfront availability of unlabeled data in the target domain, which is not practical in real-time scenarios, necessitating an advanced technique for efficient cross-domain classification of sequentially received unlabeled data.
Innovation Solution
A method and system for training a target domain classifier by extracting features from received target instances, predicting labels using a trained classifier, and updating labeled instances based on score thresholds, leveraging both source and target domain knowledge to build training data efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional supervised machine learning systems are used that rely on labeled examples, then classification accuracy can be achieved, but the system requires significant manual labeling effort and time
Solution Approach 1:
The system enables self-service by allowing the classifier to automatically generate labels for target domain instances using source domain knowledge and feature extraction, eliminating the need for manual human labeling while maintaining classification accuracy
Solution Approach 2:
The system performs preliminary action by pre-training the classifier on source domain labeled data before deployment, so that when target domain instances arrive, the classifier is already prepared to generate labels automatically without requiring real-time human intervention
2Adaptability or versatility
If domain adaptation and transfer learning techniques are used to leverage source domain labeled data, then the model can learn for unlabeled target domain data, but the assumption of upfront availability of target domain data is not met in real-time scenarios
Solution Approach 1:
The system implements dynamics by transitioning from static batch processing (assuming upfront data availability) to dynamic sequential processing where the classifier adapts in real-time as target domain instances arrive one at a time, maintaining cross-domain classification capability while enabling real-time operation
Solution Approach 2:
The system uses feedback by continuously updating the classifier with newly labeled target domain instances, where each predicted label feeds back into the training set, allowing the model to adapt and improve its cross-domain classification performance incrementally in real-time
3Loss of information
If all extracted features are used for label prediction, then comprehensive information is utilized, but the system complexity and computational overhead increase
Solution Approach 1:
The system applies taking out by selectively extracting only the most relevant features from target domain instances that are useful for classification, rather than processing all possible features, thereby reducing computational complexity while maintaining effective use of informative features
Solution Approach 2:
The system implements local quality by applying different processing strategies to different features based on their relevance and reliability, rather than treating all features uniformly, which optimizes the balance between information utilization and system complexity
Data Source
AI summary
The disclosed embodiments illustrate method and system of processing data by a computing device for training a target domain classifier. The method includes extracting one or more first features and one or more second features from a first target instance, associated with a target domain. The method further includes predicting a first label for the received first target instance based on the one or more first features by utilizing a trained first classifier associated with a set of labeled source instances, wherein the predicted first label is assigned to the first target instance when a first score of the predicted first label exceeds a first pre-specified threshold. Further, the method includes updating a set of labeled target instances associated with the target domain based on the labeled first target instance, wherein the updated set of labeled target instances is utilized to train the target domain classifier.