Artificial Intelligent Systems and Machine Learning

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Comparative Study on Feature Selection Methods to Reduce High Dimensionality in Big Data .


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-Big information may be a combination of structured, semi structured and unstructured information collected by organizations that may be strip-mined for info and employed in machine learning comes, prognosticative modeling and different advanced analytics application. Spatiality in statistics refers to what number attributes a dataset has, care information is ill-famed for having Brobdingnagian amounts of variables in a perfect world; this information may be depicted in a very unfold sheet, with one column representing every dimension. In observe, this can be tough to try to, in past as a result of several variables square measure inter-related (like weight and blood pressure).This paper gift study on feature choice technique to cut back high spatiality issue in huge information.

Keywords

Big Data, High Dimensionality, Feature Selection, Filter, Wrapped, Embedded, Hybrid.
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Abstract Views: 138




  • Comparative Study on Feature Selection Methods to Reduce High Dimensionality in Big Data .

Abstract Views: 138  | 

Authors

Dr. S. Banumathi
no, India
A. Balasathya
no, India

Abstract


-Big information may be a combination of structured, semi structured and unstructured information collected by organizations that may be strip-mined for info and employed in machine learning comes, prognosticative modeling and different advanced analytics application. Spatiality in statistics refers to what number attributes a dataset has, care information is ill-famed for having Brobdingnagian amounts of variables in a perfect world; this information may be depicted in a very unfold sheet, with one column representing every dimension. In observe, this can be tough to try to, in past as a result of several variables square measure inter-related (like weight and blood pressure).This paper gift study on feature choice technique to cut back high spatiality issue in huge information.

Keywords


Big Data, High Dimensionality, Feature Selection, Filter, Wrapped, Embedded, Hybrid.

References