Feature Selection Algorithms Using Rough Set Theory

Caballero, Yailé; Alvarez, Delia P.; Bello, Rafael; García, María M.

doi:10.1109/isda.2007.70

Cited by 27 publications

(9 citation statements)

References 15 publications

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“…Fuzzy rough set [28] is a generalization of the lower and upper approximation of the rough set [31] to allow soft boundaries. The thinking has been changed from objects which are indistinguishable (according to their attribute values) to objects similarity.…”

Section: B Fuzzy Rough Setsmentioning

confidence: 99%

“…For example, Genetic Algorithms (GAs) [5] [8] [27] [32] were used for optimization and search problems. Rough sets [31] and Artificial Neural Networks [18] are very good tools for classification and prediction problems. They are efficient in dealing with discrete data, however the real world is dealing with values like tall, short, normal, up normal and so on.…”

Section: Introductionmentioning

confidence: 99%

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A Fuzzy Rough Rule Based System Enhanced By Fuzzy Cellular Automata

Gamal¹,

El-Fetouh²,

Barakat³

2013

IJACSA

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Abstract-Handling uncertain knowledge is a very tricky problem in the current world as the data, we deal with, is uncertain, incomplete and even inconsistent. Finding an efficient intelligent framework for this kind of knowledge is a challenging task. The knowledge based framework can be represented by a rule based system that depends on a set of rules which deal with uncertainness in the data. Fuzzy rough rules are a good competitive in dealing with the uncertain cases. They are consisted of fuzzy rough variables in both the propositions and consequences. The fuzzy rough variables represent the lower and upper approximations of the subsets of a fuzzy variable. These fuzzy variables use labels (fuzzy subsets) instead of values. An efficient fuzzy rough rule based system must depend on good and accurate rules. This system needs to be enhanced to view the future recommendations or in other words the system in time sequence. This paper tries to make a rule based system for uncertain knowledge using fuzzy rough theory to generate the desired accurate rules and then use fuzzy cellular automata parallel system to enhance the rule based system developed and find out what the system would look like in time sequence so as to give good recommendations about the system in the future. The proposed model along with experimental results and simulations of the rule based systems of different data sets in time sequence is illustrated.

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Section: B Fuzzy Rough Setsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Fuzzy Rough Rule Based System Enhanced By Fuzzy Cellular Automata

Gamal¹,

El-Fetouh²,

Barakat³

2013

IJACSA

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“…This means that the original feature space is transformed into a reduced, informative new feature space [11]. In this method, attributes are combined into a new reduced set of features.…”

Section: Feature Extractionmentioning

confidence: 99%

Rough-FS

Sahoo

Mishra

2012

Proceedings of the CUBE International Information Technology Conference

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Feature selection is a process to select a subset of required features which has the same or nearly same predictive capability as that of the original feature set. Feature selection is a necessary task to be accomplished before classification process, as it becomes difficult to train a classifier for a data set having high dimension (features) and hence, cannot give optimum result. Rough Set Theory (RST) is a mathematical approach for feature selection which does not require any additional information about the data. It has the capability to extract the relevant features smoothly from a high dimensional data set having noise, imprecise, vague and redundant information. In this paper, different high dimensional gene expression data sets are used for feature selection. Using the proposed rough set algorithm, feature selection has been done and a comparative study has been done on the accuracies of the classifiers before and after reducing the features of the data. Also the generalization capability of this algorithm has been shown by taking gene expression data set along with a data set from the area of physics.

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“…RSM is used for further subset selection and rule generation and classification. RSM can also be used as a feature selection algorithm [7–10] while fuzzy logic as a classifier [11–13]. …”

Section: Introductionmentioning

confidence: 99%

Hybrid Mammogram Classification Using Rough Set and Fuzzy Classifier

Abu-Amara

Abdel‐Qader

2009

International Journal of Biomedical Imaging

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We propose a computer aided detection (CAD) system for the detection and classification of suspicious regions in mammographic images. This system combines a dimensionality reduction module (using principal component analysis), a feature extraction module (using independent component analysis), and a feature subset selection module (using rough set model). Rough set model is used to reduce the effect of data inconsistency while a fuzzy classifier is integrated into the system to label subimages into normal or abnormal regions. The experimental results show that this system has an accuracy of 84.03% and a recall percentage of 87.28%.

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Feature Selection Algorithms Using Rough Set Theory

Cited by 27 publications

References 15 publications

A Fuzzy Rough Rule Based System Enhanced By Fuzzy Cellular Automata

A Fuzzy Rough Rule Based System Enhanced By Fuzzy Cellular Automata

Rough-FS

Hybrid Mammogram Classification Using Rough Set and Fuzzy Classifier

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