A Comparison of Machine Learning Classifiers

Phani Srikanth; Amarjot Singh; Devinder Kumar; Aditya Nagrare; Vivek Angoth

doi:10.4028/www.scientific.net/AMR.271-273.149

Paper Titles

Adaptive Framework for Online Deployment of Virtual Scenes on Heterogeneous Applications
p.125

Research of Facial Features Extraction Based on Improved ASM
p.131

Design for AGV Based on Binocular Stereo Vision
p.137

A 3D Perspective Display Cube for Multiview Tracking
p.144

A Comparison of Machine Learning Classifiers
p.149

Text Summarization Using Hybrid Parallel Genetic Algorithm
p.154

Tau and Depth Cues Influence the Position of Braking in Virtual Environment
p.158

Face Recognition Based on Template Matching and Skin-Color Segmentation
p.165

Multiview Registration of Surfaces Minimizing Error Propagation
p.171

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 271-273A Comparison of Machine Learning Classifiers

A Comparison of Machine Learning Classifiers

Abstract:

A number of different classifiers have been used to improve the precision and accuracy and give better classification results. Machine learning classifiers have proven to be the most successful techniques in majority of the fields. This paper presents a comparison of the three most successful machine learning classification techniques SVM, boosting and Local SVM applied to a cancer dataset. The comparison is made on the basis of precision and accuracy along with the training time analysis. Finally, the efficacy of the classifiers is found.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 271-273)

Pages:

149-153

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.271-273.149

Citation:

Cite this paper

Online since:

July 2011

Authors:

Phani Srikanth, Amarjot Singh, Devinder Kumar, Aditya Nagrare, Vivek Angoth

Keywords:

Boosting, Cancer, Local SVM, Support Vector Machine (SVM)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Proceedings of the Thirteenth International Conference on Machine Learning, p.148–156 (1996).

Google Scholar

[2] Vapnik, V., Statistical Learning Theory, Wiley, New York, NY, (1998).

Google Scholar

[3] Ando, T., Suguro, M., Hanai, T., Kobayashi, T., Honda, H. and Seto, M., Fuzzy neural network applied to gene expression profiling for predicting the prognosis of diffuse large B-cell lymphoma, Jpn. J. Cancer Res., 93: 1207-1212, (2002).

DOI: 10.1111/j.1349-7006.2002.tb01225.x

Google Scholar

[4] H. Takahashi, H. Honda, A new reliable cancer diagnosis method using boosted fuzzy classiﬁer with SWEEP operator method, J. Chem. Eng. Jpn. 38 (2005) 763–773J.

DOI: 10.1252/jcej.38.763

Google Scholar

[5] T.R. Golub, D.K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J.P. Mesirov, H. Coller M.L. Loh, J.R. Downing M.A. Caligiuri, C.D. Bloom-ﬁeld, E.S. Lander, Molecular classiﬁcation of cancer: class discovery and class prediction by gene expression monitoring, Science 286 (1999).

DOI: 10.1126/science.286.5439.531

Google Scholar

[6] H. Takahashi, T. Kobayashi, H. Honda, Construction of robust prognostic predictors by using projective adaptive resonance theory as a gene ﬁltering method, Bioinformatics 21 (2005) 179–186.

DOI: 10.1093/bioinformatics/bth473

Google Scholar

[7] Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005).

DOI: 10.1186/1475-925x-5-51

Google Scholar

[8] Kai Ming Ting and Lian Zhu, Boosting Support Vector Machines Successfully, LNCS 5519, p.509–518, (2009).

Google Scholar