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Paper Details
Paper Title
Rough Set Feature Selection Using Bat Algorithm
Authors
Hemal Patel, Mansi shah
Abstract
Classification technique can solve several problems in different fields like medicine, industry, business, science. Noise random error or variance in a measured variable.Reduction is one of the most popular techniques to remove noisy data. Two reduction technique are used for it (FS) Feature Selection and (FE)Feature Extraction. Feature Selection (FS) is a solution that involves finding a subset of prominent features to improve predictive accuracy and to remove the redundant features. Rough Set Theory (RST) is a mathematical tool which deals with the uncertainty and vagueness of the decision systems.
Keywords- Classification, Particle Swarm Optimization (PSO) Rough Sets , Feature Selection (FS), Bat Algorithm (BA)
Publication Details
Unique Identification Number - IJEDR1602066Page Number(s) - 369-373Pubished in - Volume 4 | Issue 2 | April 2016DOI (Digital Object Identifier) - Publisher - IJEDR (ISSN - 2321-9939)
Cite this Article
Hemal Patel, Mansi shah, "Rough Set Feature Selection Using Bat Algorithm", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.4, Issue 2, pp.369-373, April 2016, Available at :http://www.ijedr.org/papers/IJEDR1602066.pdf
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