DocumentCode :
3181147
Title :
Dimensionality reduction using genetic algorithm and fuzzy-rough concepts
Author :
Saha, Moumita ; Sil, Jaya
Author_Institution :
Comput. Sci. & Eng. Dept., Bengal Eng. & Sci. Univ., Shibpur, India
fYear :
2011
fDate :
11-14 Dec. 2011
Firstpage :
379
Lastpage :
384
Abstract :
Real-world datasets are often vague and redundant, creating problem to take decision accurately. Very recently, Rough-set theory has been used successfully for dimensionality reduction but is applicable only on discrete dataset. Discretisation of data leads to information loss and may add inconsistency in the datasets. The paper aims at developing an algorithm using fuzzy-rough concept to overcome this situation. By this approach, dimensionality of the dataset has been reduced and using genetic algorithm, an optimal subset of attributes is obtained, sufficient to classify the objects. The proposed algorithm reduces dimensionality to a great extent without degrading the accuracy of classification and avoid of being trapped at local minima. Results are compared with the existing algorithms demonstrate compatible outcome.
Keywords :
data integrity; fuzzy set theory; genetic algorithms; rough set theory; data discretisation; data inconsistency; dataset dimensionality reduction; dimensionality reduction; fuzzy-rough concept; genetic algorithm; information loss; optimal subset; Approximation methods; Communications technology; Fuzzy sets; Genetic algorithms; Information systems; Rough sets;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information and Communication Technologies (WICT), 2011 World Congress on
Conference_Location :
Mumbai
Print_ISBN :
978-1-4673-0127-5
Type :
conf
DOI :
10.1109/WICT.2011.6141276
Filename :
6141276
Link To Document :
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