A Novel Two Step Genetic Program for High- Dimensional Data

  IJCOT-book-cover
 
International Journal of Computer & Organization Trends  (IJCOT)          
 
© 2016 by IJCOT Journal
Volume - 6 Issue - 6
Year of Publication : 2016
AuthorsMandava Mamatha, Ms.J. Rama Devi
  10.14445/22492593/IJCOT-V37P301

MLA

Mandava Mamatha, Ms.J. Rama Devi"A Novel Two Step Genetic Program for High- Dimensional Data", International Journal of Computer & organization Trends (IJCOT), V6(6):1-4 Nov - Dec 2016, ISSN:2249-2593, www.ijcotjournal.org. Published by Seventh Sense Research Group.

Abstract The increasing volume of data to be analysed imposes new challenges to the data mining methodologies. Now a day’s datasets have the problem curse of dimensionality. To solve these problems by the techniques PCA, 2SGP (Two Step Genetic Programming) are popular tools for linear dimensionality reduction and feature extraction. These traditional data mining methods are not integrate well with larger data sizes, miss the accuracy in terms of memory and time and not support for non-linear data. So, we propose a novel 2SKPCA algorithm .i.e; KPCA (Kernel Principal Component Analysis) and GP (Genetic Programming) to deal with high-dimensional non linear data also and reduce the dimensions. KPCA is the linear, nonlinear form of PCA, which better exploits the complicated spatial structure of highdimensional features. GP produces the feature selections and derives the significant features from the original features.

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Keywords-
Feature extraction, high-dimensional data, kernel PCA, Genetic Programming, Classification.