Multi-Tier Email Classification Framework for Real-Time Accuracy
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Solution Overview
Problem
Current systems fail to accurately and efficiently classify emails in real-time due to the sensitive nature of email data, data privacy concerns, high email traffic volume, and latency issues, leading to reduced user engagement and resource inefficiencies in mail servers.
Innovation Solution
A multi-tiered classification framework utilizing an offline grid classifier for higher accuracy and an online classifier for real-time processing, enabling multi-class labeling of emails through a two-tiered approach with BERT and logistic regression/CNN models, respectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional single-tier classification systems are used, then device complexity is reduced, but measurement precision and manufacturing precision of email classification deteriorate
Solution Approach 1:
The classification system is divided into two independent tiers: an offline grid classifier for training and model generation, and an online classifier for real-time email classification. This segmentation allows each tier to be optimized for its specific function, achieving high precision without requiring the entire system to be overly complex.
Solution Approach 2:
The offline classifier performs preliminary actions by pre-processing email data, generating training models, and creating classification rules before the online classification phase. This preliminary preparation enables the online classifier to achieve high accuracy with simpler real-time operations.
2Productivity
If high-volume email processing is implemented, then productivity increases, but loss of time due to latency increases
Solution Approach 1:
Processing is segmented into offline batch processing for model training and online real-time processing for classification. The offline tier handles heavy computational loads without time constraints, while the online tier processes emails rapidly with minimal latency, achieving both high productivity and low latency.
Solution Approach 2:
Computational preparations including feature extraction, model training, and rule generation are performed in advance during the offline phase. This preliminary action transfers computational burden from the time-critical online phase to the flexible offline phase, reducing online latency while maintaining high processing volume.
3Loss of time
If real-time classification is implemented, then loss of time is reduced, but manufacturing precision of classification deteriorates
Solution Approach 1:
The system segments classification tasks into offline model training (prioritizing accuracy) and online inference (prioritizing speed). The offline grid classifier uses comprehensive data and iterative optimization for high accuracy, while the online classifier applies pre-trained models for rapid real-time classification.
Solution Approach 2:
Accurate classification models and rules are prepared in advance during the offline phase using extensive training data and iterative optimization. This preliminary preparation of high-accuracy models enables the online phase to achieve both real-time performance and high classification accuracy without compromising precision for speed.
Data Source
AI summary
Disclosed are systems and methods for improving interactions with and between computers in content providing and/or hosting systems supported by or configured with devices, servers and/or platforms. The disclosed systems and methods provide a novel framework that automatically labels and classifies incoming emails. The disclosed framework embodies a novel computerized taxonomy configured as a multi-tier, multi-label classification system. The first tier involves an offline grid classifier that has higher accuracy, and the second tier is an online classifier that classifies emails in real-time. Thus, the framework provides a novel approach to classifying messages based on a multi-tiered analysis, which is utilized for generating user profiles, delivering the messages, and the like.