A Novel Two Step Genetic Program for High- Dimensional Data

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
Authors :  Mandava Mamatha, Ms.J. Rama Devi
DOI : 10.14445/22492593/IJCOT-V37P301

Citation

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.

References

[1] Aronis, J ., Kolluri, V., Provost, F .,& Buchanan, B. The WORLD: Knowledge discovery from multiple distributed databases. In proceedings of 10th international Florida AI Research symposium.
[2] Hubert, M., Rousseeuw, P. J., & Vanden Branden,K.ROBPCA: a new approach to robust principal component analysis.Technometrics,
[3] Rosipal, R., Girolami, M., Trejo, L. J., & Cichocki, A. (2001). Kernel PCA for feature extraction and de-noising in nonlinear regression. Neural Computing & Applications,
[4] Zhang, Y., & Bhattacharyya, S. (2004). Genetic programming in classifying large-scale data: an ensemble method. Information Sciences,
[5] Chan, P. K., & Stolfo, S. J. (1998). Toward Scalable Learning with Non-Uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection. In KDD (Vol. 1998, 164-168).
[6] Koza, J. R. (1992). Genetic programming: on the programming of computers by means of natural selection (Vol. 1). MIT press.
[7] Kumar, P., & Pandey, K. (2013). Big Data and Distributed Data Mining: An Example of Future Networks. International Journal, 2, 36-39.
[8] Koldovsky, Z., Tichavsky, P., & Oja, E. (2006). Efficient Variant of Algorithm FastICA for Independent Component Analysis Attaining CramÉr-Rao Lower Bound. Neural Networks, IEEE Transactions on, 17(5), 1265-1277.
[9] Du, Q., & Kopriva, I. (2008). Automated target detection and discrimination using constrained kurtosis maximization. Geoscience and Remote Sensing Letters, IEEE, 5(1), 38-42.
[10] Katal, A., Wazid, M., & Goudar, R. H. (2013). Big data: Issues, challenges, tools and Good practices. In Contemporary Computing (IC3), 2013 Sixth International Conference on, 404- 409.

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