@misc{10481/71037,
year = {2021},
month = {7},
url = {http://hdl.handle.net/10481/71037},
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 classification.
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.},
organization = {Ministerio de Ciencia, Innovacion y Univesidades	PID2020-119478GB-I00},
organization = {Junta de Andalucia	P18-FR-4961},
organization = {Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)	2015/20606-6},
publisher = {Elsevier},
keywords = {Multiple instance classification},
keywords = {Data Preprocessing},
keywords = {Noisy Data},
keywords = {Instance noise},
keywords = {Noise filtering},
title = {Multiple instance classification: Bag noise filtering for negative instance noise cleaning},
doi = {10.1016/j.ins.2021.07.076},
author = {Luengo Martín, Julián and Herrera Triguero, Francisco},
}