Data Classification Based on GEPSVM using Backtracking Search Algorithm
Abstract
Generalized Eigenvalue Proximal Support Vector Machine (GEPSVM) is an extremely fast and simple algorithm for generating linear and nonlinear classifiers. Kernel functions are essential in fitting GEPSVM. Usually a single kernel is used by most researchers in their studies, but the real world applications may require a combination of multiple kernel functions. There are two kind of kernels which known as global and local kernels. Global kernel functions have good generalization ability, but low learning ability. Local kernel functions have good learning ability with weak generalization. The presented approach constructs a mixed kernel function with better performance by fully combining local kernel function for strong learning ability and global kernel function for strong generalization. The Backtracking Search Algorithm (BSA) is used for determining the best value of the weight parameter between the two kernels. To evaluate the performance of the proposed approach, we applied it to public datasets from UCI repository.
Keywords
Full Text:
PDFReferences
H. Yasin, T. A. Jilani, and M. Danish, “Hepatitis-C Classification using Data Mining Techniques”, International Journal of Computer Applications, vol. 24, No. 3, pp. 1-6, 2011.
M.H. Marghny, and I.E. El-Semman, “Extracting logical classification rules with gene expression programming: microarray case study”, Proceedings of the International Conference on Artificial Intelligence and Machine Learning (AIML 05), Cairo, Egypt, pp.11–16, 2005.
M.H. Marghny, and I.E. El-Semman, “Extracting fuzzy classification rules with gene expression programming”, Proceedings of the International Conference on Artificial Intelligence and Machine Learning (AIML 05), Cairo, Egypt, 2005.
C. C. Aggarwal and C. K Reddy, “Data clustering: algorithms and applications”, Chapman and Hall/CRC Press, 2013.
M. H. Marghny and Ahmed I. Taloba, “Outlier Detection using Improved Genetic K-means”, International Journal of Computer Applications, vol. 28, No. 11, pp. 33-36, 2011.
M. H. Marghny, Rasha M. Abd El-Aziz and Ahmed I. Taloba, “An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study”, Computer Science Department, Egypt, International Journal of Computer Applications, vol. 34, No. 6, pp. 0975-8887, 2011.
Adel A. Sewisy, M. H. Marghny, Rasha M. Abd ElAziz and Ahmed I. Taloba, “Fast Efficient Clustering Algorithm for Balanced Data” International Journal of Advanced Computer Science and Applications, vol. 5, No. 6, pp. 123-129, 2014.
M. H. Marghny, M. M. Abdelsamea, "An efficient clustering based texture feature extraction for medical image", Computer and Information Technology, ICCIT, pp.88-93, 2008.
M.H. Marghny, H.E. Refaat, “A new parallel association rule mining algorithm on distributed shared memory system”, International Journal of Business Intelligence and Data Mining, vol. 7, No. 4, pp. 233-252, 2012.
M.H. Marghny, A.A. Shakour, “Fast, Simple and Memory Efficient Algorithm for Mining Association Rules”, International Review on Computers & Software, vol. 47, No. 17, pp. 3180–3192, 2007.
M.H. Marghny, “Rules extraction from constructively trained neural networks based on genetic algorithms”, International Review on Computers & Software, vol. 2, No. 1, 2007.
J. Han, and M. Kamber, "Data Mining: concepts and techniques", Morgan Kaufmann, 2001.
V. Vapnik, “The nature of statistical learning theory”, Springer, 2000.
O. L. Mangasarian, and W. W. Edward, “Multisurface proximal support vector machine classification via generalized eigenvalues”, Pattern Analysis and Machine Intelligence, IEEE Transactions, vol. 28, no. 1, pp. 69-74, 2006.
M. G. Genton, “Classes of kernels for machine learning”, a statistics perspective, The Journal of Machine Learning Research, vol. 2, pp. 299-312, 2002.
H. Huang, S. Ding, F. Jin, J. Yu, and Y. Han, “A Novel Granular Support Vector Machine Based on Mixed Kernel Function”, International Journal of Digital Content Technology and its Applications (JDCTA), vol. 6, no. 20, pp. 484-492, 2012.
S. Ding, Y. Zhang, X. Xu, and L. Bao “A novel extreme learning machine based on hybrid kernel function”, Journal of Computers vol. 8, no. 8, pp. 2110-2117, 2013.
L. Dioșan, M. Oltean, A. Rogozan, and J. Pecuchet, “Improving SVM Performance Using a Linear Combination of Kernels”, Adaptive and Natural Computing Algorithms, Springer Berlin Heidelberg, vol. 4432, pp. 218–227, 2007.
Y. Jiang, C. Tang, C. Li, S. Li, S. Ye, T. Li, and H. Zheng, “Automatic SVM Kernel Function Construction Based on Gene Expression Programming”, Proceedings of International Conference on Computer Science and Software Engineering, vol. 4, pp. 415-418, 2008.
L. Dioșan, A. Rogozan, and J. P. Pecuchet, “Improving classification performance of support vector machine by genetically optimising kernel shape and hyper-parameters”, Applied Intelligence, vol. 36, no. 2, pp. 280–294, 2012.
M. H. Marghny, Rasha M. Abd El-aziz, and Ahmed I. Taloba, “Differential Search Algorithm-based Parametric Optimization of Fuzzy Generalized Eigenvalue Proximal Support Vector Machine”, International Journal of Computer Applications, vol. 108, no.19, pp. 38-46, 2014.
G. Mercier, and M. Lennon, “Support vector machines for hyperspectral image classification with spectral-based kernels”, IEEE International, vol. 1, pp. 288-290, 2003.
A. Kumar, S. K. Ghosh, and V. K. Dadhwal, “Study of mixed kernel effect on classification accuracy using density estimation”, Proceedings of the ISPRS Commission VII Symposium, vol. xxxvi, Part 7, 2006.
P. Civicioglu, “Backtracking Search Optimization Algorithm for numerical optimization problems”, Applied Mathematics and Computation, vol. 219, pp. 8121–8144, 2013.
B. Catherine, and C. J. Merz, "UCI Repository of machine learning databases", 1998.
Information concerning hepatitis C, “www.medicalnewstoday.com/articles/145869.php”.
M.S. Bascil, and H. Oztekin, “A study on hepatitis disease diagnosis using probabilistic neural network”, MEDICAL SYSTEMS, vol. 36, no. 3, pp. 1603–1606, 2012.
D. Calisir, and E. Dogantekin, “A new intelligent hepatitis diagnosis system: Pca-lssvm”, Expert Systems with Applications, vol. 38, no. 8, pp.10705–10708, 2011.
H. L. Chen, D.Y. Liu, B. Yang, J. Liu, and G. Wang. “A new hybrid method based on local fisher discriminant analysis and support vector machines for hepatitis disease diagnosis”, Expert Systems with Applications, vol. 38, no. 9, pp.11796–11803, 2011.
S. W. Lin, and S. C. Chen, “Parameter determination and feature selection for c4.5 algorithm using scatter search approach”, Soft Comput, vol. 16, no. 1, pp. 63–75, 2012.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.