NDLI: Ensembles of label noise filters: a ranking approach

Content Provider	Springer Nature Link
Author	Garcia, Luís P. F. Lorena, Ana C. Matwin, Stan Carvalho, André C. P. L. F.
Copyright Year	2016
Abstract	Label noise can be a major problem in classification tasks, since most machine learning algorithms rely on data labels in their inductive process. Thereupon, various techniques for label noise identification have been investigated in the literature. The bias of each technique defines how suitable it is for each dataset. Besides, while some techniques identify a large number of examples as noisy and have a high false positive rate, others are very restrictive and therefore not able to identify all noisy examples. This paper investigates how label noise detection can be improved by using an ensemble of noise filtering techniques. These filters, individual and ensembles, are experimentally compared. Another concern in this paper is the computational cost of ensembles, once, for a particular dataset, an individual technique can have the same predictive performance as an ensemble. In this case the individual technique should be preferred. To deal with this situation, this study also proposes the use of meta-learning to recommend, for a new dataset, the best filter. An extensive experimental evaluation of the use of individual filters, ensemble filters and meta-learning was performed using public datasets with imputed label noise. The results show that ensembles of noise filters can improve noise filtering performance and that a recommendation system based on meta-learning can successfully recommend the best filtering technique for new datasets. A case study using a real dataset from the ecological niche modeling domain is also presented and evaluated, with the results validated by an expert.
Starting Page	1192
Ending Page	1216
Page Count	25
File Format	PDF
ISSN	13845810
Journal	Data Mining and Knowledge Discovery
Volume Number	30
Issue Number	5
e-ISSN	1573756X
Language	English
Publisher	Springer US
Publisher Date	2016-07-13
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Label noise Noise filters Ensemble filters Noise ranking Recommendation system Data Mining and Knowledge Discovery Artificial Intelligence (incl. Robotics) Information Storage and Retrieval Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Information Systems Computer Science Applications

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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