An Optimal Reject Rule for Binary Classifiers
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
The Entire Regularization Path for the Support Vector Machine
The Journal of Machine Learning Research
A boosting approach for motif modeling using ChIP-chip data
Bioinformatics
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
An Interior-Point Method for Large-Scale l1-Regularized Logistic Regression
The Journal of Machine Learning Research
Algorithms for Sparse Linear Classifiers in the Massive Data Setting
The Journal of Machine Learning Research
Classification with a Reject Option using a Hinge Loss
The Journal of Machine Learning Research
Fixed-Point Continuation for $\ell_1$-Minimization: Methodology and Convergence
SIAM Journal on Optimization
On optimum recognition error and reject tradeoff
IEEE Transactions on Information Theory
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In cancer classification based on gene expression data, it would be desirable to defer a decision for observations that are difficult to classify. For instance, an observation for which the conditional probability of being cancer is around 1/2 would preferably require more advanced tests rather than an immediate decision. This motivates the use of a classifier with a reject option that reports a warning in cases of observations that are difficult to classify. In this paper, we consider a problem of gene selection with a reject option. Typically, gene expression data comprise of expression levels of several thousands of candidate genes. In such cases, an effective gene selection procedure is necessary to provide a better understanding of the underlying biological system that generates data and to improve prediction performance. We propose a machine learning approach in which we apply the l"1 penalty to the SVM with a reject option. This method is referred to as the l"1 SVM with a reject option. We develop a novel optimization algorithm for this SVM, which is sufficiently fast and stable to analyze gene expression data. The proposed algorithm realizes an entire solution path with respect to the regularization parameter. Results of numerical studies show that, in comparison with the standard l"1 SVM, the proposed method efficiently reduces prediction errors without hampering gene selectivity.