International Journal of Application or Innovation in Engineering & Management
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ISSN 2319 – 4847
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Call for Paper, Published Articles, Indexing Infromation Survey on Feature Selection in High-dimensional data via Constraint, Relevance and Redundancy, Authors : N.Anitha and S.Deepa, International Journal of Application or Innovation in Engineering & Management (IJAIEM), www.ijaiem.org
Volume & Issue no: Volume 3, Issue 9, September 2014

Title:
Survey on Feature Selection in High-dimensional data via Constraint, Relevance and Redundancy
Author Name:
N.Anitha and S.Deepa
Abstract:
ABSTRACT Machine learning provides tools by which large qualities of data can be automatically analyzed. Fundamental to machine learning is feature selection. Feature selection by identifying the most salient features of learning, focuses a learning algorithm on those aspects of the data most useful for analysis for feature prediction. Feature selection as a preprocessing step to machine learning, has been effectively in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improving comprehensibility. Most feature selection methods focus on finding relevant features for optimizing highdimensional data. Dimensionality reduction is a significant task dealing with high dimensional data. Semi supervised clustering aims to improve the clustering performance by considering the pair-wise constraints. Semi supervised feature selection method is most efficient for finding the relevant features, eliminating the redundant features. Keywords:- Feature selection, Dimensionality reduction, Semi supervised, Constraint, Relevance, and Redundancy
Cite this article:
N.Anitha and S.Deepa , " Survey on Feature Selection in High-dimensional data via Constraint, Relevance and Redundancy " , International Journal of Application or Innovation in Engineering & Management (IJAIEM), Volume 3, Issue 9, September 2014 , pp. 244-247 , ISSN 2319 - 4847.
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