Here are the links to the datasets you will use in your feature selection experiments:

Here are the links to the datasets you will use in your experiments:

• Gisette
• Dexter
• Arcene

• Hui Zou and Trevor Hastie. Regularization and Variable Selection via the Elastic Net. JRSSB (2005) 67(2) 301-320. An R package elasticnet is available from CRAN. (Majdi)

• T. Joachims. A Support Vector Method for Multivariate Performance Measures. Proceedings of the International Conference on Machine Learning (ICML), 2005. code. Can be used for feature selection like SVMs are used in the RFE method.

• Wei Bi and James Kwok. Multi-Label Classification on Tree- and DAG-Structured Hierarchies. International Conference on Machine Learning (ICML-11), 2011. (Indika)

• T. Joachims. A Support Vector Method for Multivariate Performance Measures. Proceedings of the International Conference on Machine Learning (ICML), 2005. code. Can be used for feature selection like SVMs are used in the RFE method. (Majdi)

• Yeung K, Bumgarner R. Multiclass classification of microarray data with repeated measurements: application to cancer. Genome Biology. 4:R83, 2003. (Simon)

• Y. Sun, S. Todorovic, and S. Goodison. Local Learning Based Feature Selection for High Dimensional Data Analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), vol. 32, no. 9, pp. 1610-1626, 2010. (Nand)

• Ji Zhu, Saharon Rosset, Trevor Hastie and Rob Tibshirani. 1-norm support vector machines. In: Neural Information Processing Systems (NIPS) 16, 2004. 1-norm classifiers create very sparse representations, so are useful for feature selection. (Prathamesh)

• Feiping Nie, Heng Huang, Cai Xiao, Chris Ding. Efficient and Robust Feature Selection via Joint L2,1-Norms Minimization. NIPS 2010. (Rehab)

• Chris Ding and Hanchuan Peng. Minimum redundancy feature selection from microarray gene expression data. Journal of Bioinformatics and Computational Biology, Vol. 3, No. 2, pp.185-205, 2005. (Joel)

• T. Joachims. A Support Vector Method for Multivariate Performance Measures. Proceedings of the International Conference on Machine Learning (ICML), 2005. code. Can be used for feature selection like SVMs are used in the RFE method.

• T. Joachims. A Support Vector Method for Multivariate Performance Measures. Proceedings of the International Conference on Machine Learning (ICML), 2005. code. Can be used for feature selection like the RFE method.

• Ji Zhu, Saharon Rosset, Trevor Hastie and Rob Tibshirani. 1-norm support vector machines. In: Neural Information Processing Systems (NIPS) 16, 2004. 1-norm classifiers create very sparse representations, so are useful for feature selection.

• Christoph H. Lampert. Maximum Margin Multi-Label Structured Prediction. Neural Information Processing Systems (NIPS), 2011.

Prediction of protein function

• Christoph H. Lampert. [[http://books.nips.cc/papers/files/nips24/NIPS2011_0207.pdf | Maximum Margin Multi-Label Structured Prediction. Neural Information Processing Systems(NIPS), 2011.

• T. Joachims. A Support Vector Method for Multivariate Performance Measures. Proceedings of the International Conference on Machine Learning (ICML), 2005. code. Can be used for feature selection like the RFE method.

• A. Rakotomamonjy. Optimizing AUC with SVMs. Proceedings of European Conference on Artificial Intelligence Workshop on ROC Curve and AI, Valencia, 2004. code. Can be used for feature selection like the RFE method.

• A. Rakotomamonjy. Optimizing AUC with SVMs. Proceedings of European Conference on Artificial Intelligence Workshop on ROC Curve and AI, Valencia, 2004.

• Y. Sun, S. Todorovic, and S. Goodison. Local Learning Based Feature Selection for High Dimensional Data Analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), vol. 32, no. 9, pp. 1610-1626, 2010.

• Y. Sun, S. Todorovic, and S. Goodison. [[http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.163.2272

 | Local Learning Based Feature Selection for High Dimensional Data Analysis]].  IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), vol. 32, no. 9, pp. 1610-1626, 2010.

• Yeung K, Bumgarner R. Multiclass classification of microarray data with repeated measurements: application to cancer. Genome Biology. 4:R83, 2003.

• Yeung K, Bumgarner R. [[http://genomebiology.com/2003/4/12/r83

 | Multiclass classification of microarray data

with repeated measurements: application to cancer]]. Genome Biol. 4:R83, 2003.

• Ji Zhu, Saharon Rosset, Trevor Hastie and Rob Tibshirani. 1-norm support vector machines. In: Neural Information Processing Systems (NIPS) 16, 2004.

• Ji Zhu, Saharon Rosset, Trevor Hastie and Rob Tibshirani. 1-norm support vector machines. In: Neural Information

• Hui Zou and Trevor Hastie. Regularization and Variable Selection via the Elastic Net. JRSSB (2005) 67(2) 301-320. An R package elasticnet is available from CRAN.

• Hui Zou and Trevor Hastie. [[http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9868.2005.00503.x/full | Regularization and Variable Selection via

• Feiping Nie, Heng Huang, Cai Xiao, Chris Ding. Efficient and Robust Feature Selection via Joint L2,1-Norms Minimization. NIPS 2010.

• Chris Ding and Hanchuan Peng. Minimum redundancy feature selection from microarray gene expression data. Journal of Bioinformatics and Computational Biology, Vol. 3, No. 2, pp.185-205, 2005.

There are two topics available for projects: feature selection and prediction of protein function. Your first step is to choose a paper whose method you will use/implement.

Feature selection

Filter methods:

• Chris Ding and Hanchuan Peng. Minimum redundancy feature selection from microarray gene expression data.

Journal of Bioinformatics and Computational Biology, Vol. 3, No. 2, pp.185-205, 2005.

Embedded feature selection methods:

• Hui Zou and Trevor Hastie. [[http://www.stanford.edu/~hastie/Papers/B67.2%20(2005)%20301-320%20Zou%20&%20Hastie.pdf | Regularization and Variable Selection via

the Elastic Net]]. JRSSB (2005) 67(2) 301-320. An R package elasticnet is available from CRAN.

• Ji Zhu, Saharon Rosset, Trevor Hastie and Rob Tibshirani. [[http://www.stat.lsa.umich.edu/~jizhu/pubs/Zhu-NIPS04.pdf | 1-norm

support vector machines]]. In: Neural Information Processing Systems (NIPS) 16, 2004.

• Feiping Nie, Heng Huang, Cai Xiao, Chris Ding. Efficient and Robust Feature Selection via Joint L2,1-Norms Minimization. NIPS 2010.

Course projects

Coming soon!