Method and system for contradiction avoided learning for multi-class multi-label classification

US12038949B2 · kind B2 · utility

Assignee

• Tata Consultancy Services Limited · IN

Inventors

• Arijit Ukil · Sherghati, IN
• Arpan Pal · Sherghati, IN
• Soumadeep SAHA · Sherghati, IN
• Utpal Garain · Sherghati, IN

Key dates

• Filing date: Oct 26, 2023
• Grant date: Jul 16, 2024
• Expiry date: Oct 26, 2043

Classification

• Technology area (CPC G): Physics
• CPC primary: G16H50/70
• WIPO field: Medical technology
• WIPO sector: Instruments

Abstract

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.