Robust fuzzy rough classifiers
نویسندگان
چکیده
Fuzzy rough sets, generalized from Pawlak’s rough sets, were introduced for dealing with continuous or fuzzy data. This model has been widely discussed and applied these years. It is shown that the model of fuzzy rough sets is sensitive to noisy samples, especially sensitive to mislabeled samples. As data are usually contaminated with noise in practice, a robust model is desirable. We introduce a new model of fuzzy rough set model, called soft fuzzy rough sets, and design a robust classification algorithm based on the model. Experimental results show the effectiveness of the proposed algorithm. © 2011 Elsevier B.V. All rights reserved.
منابع مشابه
Supervised Learning Based on Fuzzy Sets, Rough Sets, or Hybrid Approaches
This paper focuses on supervised learning, which involves specific mechanism for building discretizers, inducers and classifiers. We discuss and compare such mechanisms based on fuzzy set theory we previously proposed, with their counterparts based on rough set theory, as well as with some possible approaches emerged by hybridization.
متن کاملAiding Fuzzy Rule Induction with Fuzzy Rough Attribute Reduction
Many rule induction algorithms are unable to cope with high dimensional descriptions of input features. To enable such techniques to be effective, a redundancy-removing step is usually carried out beforehand. Rough Set Theory (RST) has been used as such a dataset pre-processor with much success, however it is reliant upon a crisp dataset; important information may be lost as a result of quantiz...
متن کاملKernel-Based Fuzzy-Rough Nearest Neighbour Classification.dvi
Fuzzy-rough sets play an important role in dealing with imprecision and uncertainty for discrete and real-valued or noisy data. However, there are some problems associated with the approach from both theoretical and practical viewpoints. These problems have motivated the hybridisation of fuzzy-rough sets with kernel methods. Existing work which hybridises fuzzy-rough sets and kernel methods emp...
متن کاملSoft fuzzy rough sets for robust feature evaluation and selection
The fuzzy dependency function proposed in the fuzzy rough set model is widely employed in feature evaluation and attribute reduction. It is shown that this function is not robust to noisy information in this paper. As datasets in real-world applications are usually contaminated by noise, robustness of data analysis models is very important in practice. In this work, we develop a new model of fu...
متن کاملClassification of Complex Urban Fringe Land Cover Using Evidential Reasoning Based on Fuzzy Rough Set: A Case Study of Wuhan City
Urban fringe is the transition zone fine grained with urban and non-urban land cover types. The complex landscape mosaic in this area challenges the land cover classification based on the remote-sensing data. Spectral signatures are not efficient to discriminate all pixels into classes. To improve the recognition and handle the uncertainty, this paper provides a novel integrated approach, based...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Fuzzy Sets and Systems
دوره 183 شماره
صفحات -
تاریخ انتشار 2011