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Journal ArticleDOI

Parameterized attribute reduction with Gaussian kernel based fuzzy rough sets

Degang Chen, +2 more
- 01 Dec 2011 - 
- Vol. 181, Iss: 23, pp 5169-5179
TLDR
A heuristic algorithm is designed to compute reducts with Gaussian kernel fuzzy rough sets and parameterized attribute reduction with the derived model of fuzzy Rough sets is introduced.
About: 
This article is published in Information Sciences.The article was published on 2011-12-01. It has received 88 citations till now. The article focuses on the topics: Fuzzy set operations & Fuzzy number.

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Citations
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Journal ArticleDOI

Feature selection in mixed data

Xiao Zhang, +3 more
- 01 Aug 2016 - 
TL;DR: It is proved that the newly-defined entropy meets the common requirement of monotonicity and can equivalently characterize the existing attribute reductions in the fuzzy rough set theory.
Journal ArticleDOI

A Fitting Model for Feature Selection With Fuzzy Rough Sets

Changzhong Wang, +6 more
- 01 Aug 2017 - 
TL;DR: A parameterized fuzzy relation is introduced to characterize the fuzzy information granules, using which the fuzzy lower and upper approximations of a decision are reconstructed and a new fuzzy rough set model is introduced.
Journal ArticleDOI

Implementing algorithms of rough set theory and fuzzy rough set theory in the R package “RoughSets”

Lala Septem Riza, +6 more
- 10 Dec 2014 - 
TL;DR: The package RoughSets, written mainly in the R language, provides implementations of methods from the rough set theory and fuzzy rough set theories for data modeling and analysis and should be considered as an alternative software library for analyzing data based on RST and FRST.
Journal ArticleDOI

Attribute reduction for dynamic data sets

Feng Wang, +2 more
TL;DR: An attribute reduction algorithm for data sets with dynamically varying data values based on three representative entropies that can find a new reduct in a much shorter time when a part of data in a given data set is replaced by some new data.
Journal ArticleDOI

Attribute Reduction for Heterogeneous Data Based on the Combination of Classical and Fuzzy Rough Set Models

Degang Chen, +1 more
- 01 Oct 2014 - 
TL;DR: The proposed attribute reduction deals with heterogeneous condition attributes from the viewpoint of discernible ability and can consider the mutual effects between two types of attributes without preprocessing into single-typed ones.
References
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Book

The Nature of Statistical Learning Theory

Vladimir Vapnik
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Book

Pattern Classification

Peter E. Hart, +2 more
Journal ArticleDOI

An introduction to variable and feature selection

Isabelle Guyon, +1 more
- 01 Mar 2003 - 
TL;DR: The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.

UCI Repository of machine learning databases

Catherine Blake
Journal ArticleDOI

Wrappers for feature subset selection

Ron Kohavi, +1 more
- 01 Dec 1997 - 
TL;DR: The wrapper method searches for an optimal feature subset tailored to a particular algorithm and a domain and compares the wrapper approach to induction without feature subset selection and to Relief, a filter approach tofeature subset selection.