NDLI: The influence of noise on the evolutionary fuzzy systems for subgroup discovery

Content Provider	Springer Nature Link
Author	Luengo, J. García Vico, A. M. Pérez Godoy, M. D. Carmona, C. J.
Copyright Year	2016
Abstract	External factors such as the presence of noise in data can affect the data mining process. This is a common problem that produces several negative consequences which involves errors in the data collection, preparation and, above all, in the results obtained by the data mining techniques employed. The capabilities of the models built under such circumstances will depend heavily on the quality of the training data. Hence, problems containing noise are complex problems and accurate solutions are often difficult to achieve. A particular supervised learning field like subgroup discovery has overlooked the analysis of noise and its impact on the descriptions obtained. This paper presents an analysis of the impact of noise on the most relevant evolutionary fuzzy systems for subgroup discovery. We also focus on how filtering techniques, devised for predictive tasks, may alleviate the impact of noise on descriptive fields such as subgroup discovery. Specifically, the analysis is carried out using recent filtering techniques for several class noise levels. The results obtained show two different behaviours, on the one hand, the SDIGA and NMEEFSD algorithms present a decrease in the quality of the subgroups when the noise is increased, making necessary the application of noise filtering in order to compensate for this loss of quality. On the other hand, the FuGePSD algorithm demonstrates its great capacity to work in noisy environments without the necessity of using a preliminary filter. The study is completed with an analysis of the interpretability under the influence of noise focused on the number of rules and variables.
Starting Page	4313
Ending Page	4330
Page Count	18
File Format	PDF
ISSN	14327643
Journal	Soft Computing
Volume Number	20
Issue Number	11
e-ISSN	14337479
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2016-08-11
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Subgroup discovery Class noise Noise filters Evolutionary fuzzy systems Computational Intelligence Artificial Intelligence (incl. Robotics) Mathematical Logic and Foundations Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Software Geometry and Topology

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