DocumentCode :
265997
Title :
A survey of feature selection and feature extraction techniques in machine learning
Author :
Khalid, Sohail ; Khalil, Tehmina ; Nasreen, Shamila
Author_Institution :
Software Eng. Dept., Bahria Univ. Islamabad, Islamabad, Pakistan
fYear :
2014
fDate :
27-29 Aug. 2014
Firstpage :
372
Lastpage :
378
Abstract :
Dimensionality reduction as a preprocessing step to machine learning is effective in removing irrelevant and redundant data, increasing learning accuracy, and improving result comprehensibility. However, the recent increase of dimensionality of data poses a severe challenge to many existing feature selection and feature extraction methods with respect to efficiency and effectiveness. In the field of machine learning and pattern recognition, dimensionality reduction is important area, where many approaches have been proposed. In this paper, some widely used feature selection and feature extraction techniques have analyzed with the purpose of how effectively these techniques can be used to achieve high performance of learning algorithms that ultimately improves predictive accuracy of classifier. An endeavor to analyze dimensionality reduction techniques briefly with the purpose to investigate strengths and weaknesses of some widely used dimensionality reduction methods is presented.
Keywords :
data mining; feature extraction; feature selection; learning (artificial intelligence); dimensionality reduction; feature extraction techniques; feature selection; learning accuracy; learning algorithms; machine learning; pattern recognition; redundant data; Accuracy; Algorithm design and analysis; Correlation; Feature extraction; Noise; Principal component analysis; Redundancy; Age Related Macula Degeneration (AMD); Correlation Based Method; FSA´s; Feature Extraction/Transformation; Feature Selection; Feature Subset Selection; ICA; PCA; RELIEF;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Science and Information Conference (SAI), 2014
Conference_Location :
London
Print_ISBN :
978-0-9893-1933-1
Type :
conf
DOI :
10.1109/SAI.2014.6918213
Filename :
6918213
Link To Document :
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