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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Multiple instance classification: Bag noise filtering for negative instance noise cleaning

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Author(s):
Luengo, Julian [1] ; Sanchez-Tarrago, Danel [2] ; Prati, Ronaldo C. [3] ; Herrera, Francisco [1]
Total Authors: 4
Affiliation:
[1] Univ Granada, Dept Comp Sci & Artificial Intelligence, Granada 18071 - Spain
[2] Cent Univ Marta Abreu Las Villas, Dept Comp Sci, Santa Clara - Cuba
[3] Fed Univ ABC UFABC, Ctr Math Comp Sci & Cognit CMCC, Santo Andre, SP - Brazil
Total Affiliations: 3
Document type: Journal article
Source: INFORMATION SCIENCES; v. 579, p. 388-400, NOV 2021.
Web of Science Citations: 0
Abstract

Data in the real world is far from being perfect. The appearance of noise is a common issue that arises from the limitations of data acquisition mechanisms and human knowledge. In classification, label noise will hinder the performance of almost all classifiers, inducing a bias in the built model. While label noise has recently attracted researchers' attention in standard classification, it has only recently begun to be studied in multiple instance clas-sification. In this work, we propose the usage of filtering algorithms for multiple instance classification that are able to reduce the impact of negative instances within the bags. In order to do so, we decompose the bags to form a standard classification problem that can be efficiently treated by a specialized noise filter. Such a decomposition is tackled in different ways, with the aim of exploiting the knowledge offered by the examples from opposite bags. The bags are then rebuilt, without the identified noise instances. In our experiments, we show that by applying our approach we can diminish the impact of noise and even obtain better results at 0% noise level for several classifiers. Our approach sets out a promising approach to dealing with noise in the bags of multiple instance datasets and further improve the classification rate of the built models. (c) 2021 Published by Elsevier Inc. (AU)

FAPESP's process: 15/20606-6 - Noise Labels in Machine Learning: Evaluation measures and machine learning algorithms
Grantee:Ronaldo Cristiano Prati
Support Opportunities: Scholarships abroad - Research