Multiple instance classification: Bag noise filtering for negative instance noise cleaning
Metadatos
Mostrar el registro completo del ítemEditorial
Elsevier
Materia
Multiple instance classification Data Preprocessing Noisy Data Instance noise Noise filtering
Fecha
2021-07-27Referencia bibliográfica
Julián Luengo... [et al.]. Multiple instance classification: Bag noise filtering for negative instance noise cleaning, Information Sciences, Volume 579, 2021, Pages 388-400, ISSN 0020-0255, [https://doi.org/10.1016/j.ins.2021.07.076]
Patrocinador
Ministerio de Ciencia, Innovacion y Univesidades PID2020-119478GB-I00; Junta de Andalucia P18-FR-4961; Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) 2015/20606-6Resumen
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.