论文信息 - An Approach to overcome Imbalance Datasets of Eukaryotic Genomes during the Analysis by Machine Learning Technique (SVM)

An Approach to overcome Imbalance Datasets of Eukaryotic Genomes during the Analysis by Machine Learning Technique (SVM)

In biology, Support Vector Machines (SVM) is most frequently used tool for the analysis of gene expression, microarray experiments and other biological applications. In human genome dataset, only a small proportion of the DNA sequences represent genes, and the rest do not. In our work, we highlighted the reasons why, particular SVM, fails and what can be done to overcome this.

[1] Nello Cristianini,et al. Controlling the Sensitivity of Support Vector Machines , 1999 .

[2] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[3] M. Kozak,et al. Interpreting cDNA sequences: Some insights from studies on translation , 1996, Mammalian Genome.

[4] Nello Cristianini,et al. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[5] Edward Y. Chang,et al. Class-Boundary Alignment for Imbalanced Dataset Learning , 2003 .

[6] Stephen Kwek,et al. Applying Support Vector Machines to Imbalanced Datasets , 2004, ECML.

[7] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[8] Limsoon Wong,et al. Using feature generation and feature selection for accurate prediction of translation initiation sites. , 2002, Genome informatics. International Conference on Genome Informatics.