The influence of noise on the evolutionary fuzzy systems for subgroup discovery
详细信息 查看全文
- 作者：J. Luengo ; A. M. García-Vico ; M. D. Pérez-Godoy ; C. J. Carmona
- 关键词：Subgroup discovery ; Class noise ; Noise filters ; Evolutionary fuzzy systems
- 刊名：Soft Computing - A Fusion of Foundations, Methodologies and Applications
- 出版年：2016
- 出版时间：November 2016
- 年：2016
- 卷：20
- 期：11
- 页码：4313-4330
- 全文大小：625 KB
- 刊物类别：Engineering
- 刊物主题：Numerical and Computational Methods in Engineering
Theory of Computation
Computing Methodologies
Mathematical Logic and Foundations
Control Engineering - 出版者：Springer Berlin / Heidelberg
- ISSN：1433-7479
- 卷排序：20
文摘
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.