Determined Multi-Label Learning: Alleviating Labeling Costs

Основные понятия

Proposing Determined Multi-Label Learning to reduce labeling costs in multi-label tasks.

Аннотация

The content introduces Determined Multi-Label Learning (DMLL) as a novel labeling setting to reduce the annotation cost in multi-label tasks. It associates each training instance with a determined label, either "Yes" or "No," indicating the presence of a provided class label. The proposed risk-consistent estimator and similarity-based prompt enhance semantic information and improve model performance.
Abstract:

DMLL aims to reduce labeling costs in multi-label classification.
Introduces risk-consistent estimator and similarity-based prompt for improved performance.
Introduction:

Discusses challenges in multi-label learning due to labor-intensive labeling.
Proposes DMLL as an alternative approach to alleviate labeling costs.
Proposed Method:

Describes DMLL setup with determined labels for training instances.
Introduces risk-consistent estimator and similarity-based prompt for semantic enhancement.
Experiments:

Conducts experiments on benchmark datasets showcasing superior performance of DMLL.
Compares DMLL with existing weakly supervised methods, demonstrating significant improvements.

Статистика

"In multi-label classification, each training instance is associated with multiple class labels simultaneously."
"To alleviate this problem, a novel labeling setting termed Determined Multi-Label Learning (DMLL) is proposed."
"Extensive experimental validation underscores the efficacy of our approach."

Цитаты

"In this paper, we theoretically derive a risk-consistent estimator to learn a multi-label classifier from these determined-labeled training data."
"Experimental results demonstrate that our method outperforms the existing state-of-the-art weakly multi-label learning methods significantly."

Ключевые выводы из

Determined Multi-Label Learning via Similarity-Based Prompt

by Meng Wei,Zho... в arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16482.pdf

Дополнительные вопросы

How can the concept of Determined Multi-Label Learning be applied to other machine learning tasks

Determined Multi-Label Learning (DMLL) can be applied to various machine learning tasks beyond multi-label classification. One potential application is in natural language processing for sentiment analysis. Instead of labeling each text with multiple sentiments, annotators could simply determine whether a specific sentiment is present in the text or not. This approach would reduce annotation costs and streamline the training process for sentiment analysis models.

What are the potential limitations or drawbacks of using determined labels in training instances

While Determined Multi-Label Learning offers advantages in reducing labeling costs, there are some limitations to using determined labels in training instances. One drawback is that it may oversimplify the complexity of real-world data by only focusing on one label per instance. This could lead to information loss and potentially impact the model's ability to capture nuanced relationships between different labels within an instance.

How might advancements in large-scale pre-trained models impact the effectiveness of DMLL in the future

Advancements in large-scale pre-trained models can significantly impact the effectiveness of DMLL in the future. As these models continue to improve their understanding of complex relationships between features, they can provide more accurate predictions based on determined labels. Additionally, with better feature extraction capabilities from these models, DMLL algorithms can leverage richer semantic information and enhance performance across various machine learning tasks.