Kaito Shiku(Graduate School of Information Science and Electrical Engineering) paper has been accepted for Pattern Recognition.
Congratulations!
Authors
Kaito Shiku, Shinnosuke Matsuo, Daiki Suehiro, Ryoma Bise
Affiliation
Graduate School of Information Science and Electrical Engineering, Department of Information Science and Technology
Manuscript Title
Learning from Majority Label: A Novel Problem in Multi-class Multiple-Instance Learning
Abstract
The paper proposes a novel multi-class Multiple-Instance Learning (MIL) problem called Learning from Majority Label (LML). In LML, the majority class of instances in a bag is assigned as the bag-level label. The goal of LML is to train a classification model that estimates the class of each instance using the majority label. This problem is valuable in a variety of applications, including pathology image segmentation, political voting prediction, customer sentiment analysis, and environmental monitoring. To solve LML, we propose a Counting Network trained to produce bag-level majority labels, estimated by counting the number of instances in each class.
Furthermore, analysis experiments on the characteristics of LML revealed that bags with a high proportion of the majority class facilitate learning. Based on this result, we developed a Majority Proportion Enhancement Module (MPEM) that increases the proportion of the majority class by removing minority class instances within the bags. Experiments demonstrate the superiority of the proposed method on four datasets compared to conventional MIL methods. Moreover, ablation studies confirmed the effectiveness of each module.
Journal name
Pattern Recognition
Relevant SDGs
SDGs 3 (Good health and well-being)
SDGs 9 (Industry, Innovation, Technology and Infrastructure)
Comments
The paper I submitted in April has been accepted by Pattern Recognition, a leading international journal in the field of pattern recognition!
