Contradiction-Avoided Multi-Label Classification for ECG Analysis
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Solution Overview
Problem
Multi-class multi-label classification tasks face challenges due to noisy datasets and the absence of semantic information, leading to logical contradictions in predictions, especially in domains like medicine where conditions like hypertension and hypo-tension cannot co-exist, resulting in inaccurate and contradictory outputs.
Innovation Solution
A method and system for contradiction-avoided learning that constructs a binary contradiction matrix based on domain knowledge to split data instances into sets with and without contradictions, training a multi-label classification model using contradiction loss and binary cross-entropy loss to minimize logical errors, ensuring accurate and consistent predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multi-label classification is performed without incorporating semantic information or domain knowledge, then the model can process diverse labels flexibly, but logical contradictions appear in predictions (e.g., hypertension and hypo-tension simultaneously)
Solution Approach 1:
The patent applies preliminary action by constructing a contradiction matrix before training the classification model. This matrix encodes domain knowledge about which label pairs cannot co-exist (e.g., hypertension and hypo-tension). By pre-processing the data with this contradiction matrix, the model is guided to avoid contradictory predictions from the outset, rather than correcting them after generation.
Solution Approach 2:
The contradiction matrix serves as an intermediary between the raw multi-label data and the classification model. It mediates the learning process by introducing contradiction-aware loss functions that penalize contradictory label assignments. This intermediary structure allows the model to maintain flexibility with diverse labels while incorporating domain knowledge without hard-coding specific medical rules.
2Quantity of substance
If noisy datasets are used for training, then more real-world data can be processed, but classification accuracy deteriorates due to increased logical errors and contradictions
Solution Approach 1:
The patent converts the harmful effect of noisy data into a benefit by using contradiction-aware loss functions. Instead of discarding noisy data or manually cleaning it, the model learns from the noise while the contradiction matrix guides it to avoid contradictory predictions. The noise that would normally degrade accuracy is filtered through the contradiction constraints, turning potential errors into learning opportunities that reinforce logical consistency.
Solution Approach 2:
The contradiction-aware loss functions provide continuous feedback during training by penalizing contradictory label assignments. When the model generates contradictory predictions, the loss function signals this error and adjusts the model parameters to reduce future contradictions. This feedback mechanism allows the model to progressively improve accuracy even when trained on noisy real-world data.
3Ease of manufacture
If standard multi-label classification is applied without contradiction constraints, then training is simpler and faster, but the model produces unacceptable results from domain expert perspective
Solution Approach 1:
The patent changes the loss function parameters to incorporate contradiction constraints. Instead of using standard cross-entropy loss, the model uses contradiction-aware loss functions that add penalty terms for contradictory label pairs. This parameter modification maintains the overall training framework simplicity while improving domain alignment, as the contradiction matrix can be constructed once and reused throughout training.
Data Source
AI summary
This disclosure relates generally to multi-class multi-label classification and more particularly to contradiction avoided learning for multi-class multi-label classification. Conventional classification methods do not consider contradictory outcomes in multi-label classification tasks wherein contradictory outcomes have significant negative impact in the classification problem solution. The present disclosure provides a contradiction avoided learning multi-class multi-label classification. The disclosed method utilizes a binary contradiction matrix constructed using domain knowledge. Based on the binary contradiction matrix the training dataset is divided into two parts, one comprising contradictions and the second without contradictions. The classification model is trained using the divided datasets using a contradiction loss and a binary cross entropy loss to avoid contradictions during learning of the classification model. The disclosed method is used for electrocardiogram classification, shape classification and so on.