Rough ACO: A Hybridized Model for Feature Selection in Gene Expression Data | Zendy

Debahuti Mishra | Zendy; Amiya Kumar Rath | Zendy; Milu Acharya | Zendy; Tanushree Jena | Zendy

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Rough ACO: A Hybridized Model for Feature Selection in Gene Expression Data

Author(s) -

Debahuti Mishra,

Amiya Kumar Rath,

Milu Acharya,

Tanushree Jena

Publication year - 2010

Publication title -

international journal of computer and communication technology

Language(s) - English

Resource type - Journals

eISSN - 2231-0371

pISSN - 0975-7449

DOI - 10.47893/ijcct.2010.1009

Subject(s) - rough set , preprocessor , dimensionality reduction , feature selection , pattern recognition (psychology) , feature (linguistics) , curse of dimensionality , set (abstract data type) , artificial intelligence , expression (computer science) , computer science , data set , reduction (mathematics) , data mining , data pre processing , mathematics , philosophy , linguistics , programming language , geometry

Dimensionality reduction of a feature set is a common preprocessing step used for pattern recognition, classification applications and in compression schemes. Rough Set Theory is one of the popular methods used, and can be shown to be optimal using different optimality criteria. This paper proposes a novel method for dimensionality reduction of a feature set by choosing a subset of the original features that contains most of the essential information, using the same criteria as the ACO hybridized with Rough Set Theory. We call this method Rough ACO. The proposed Rough ACO: A Hybridized Model for Feature Selection in Gene Expression Data 86 method is successfully applied for choosing the best feature combinations and then applying the Upper and Lower Approximations to find the reduced set of features from a gene expression data.

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