The nature of statistical learning theory
The nature of statistical learning theory
Cancer diagnosis and prognosis via linear-programming-based machine learning
Cancer diagnosis and prognosis via linear-programming-based machine learning
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Combined SVM-Based Feature Selection and Classification
Machine Learning
Cell-nuclear data reduction and prognostic model selection in bladder tumor recurrence
Artificial Intelligence in Medicine
A Support Vector Approach to Censored Targets
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
A combined neural network and decision trees model for prognosis of breast cancer relapse
Artificial Intelligence in Medicine
Twin least squares support vector regression
Neurocomputing
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
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Prognostic prediction is important in medical domain, because it can be used to select an appropriate treatment for a patient by predicting the patient's clinical outcomes. For high-dimensional data, a normal prognostic method undergoes two steps: feature selection and prognosis analysis. Recently, the L_1\hbox{-}L_2-norm Support Vector Machine (L_1\hbox{-}L_2 SVM) has been developed as an effective classification technique and shown good classification performance with automatic feature selection. In this paper, we extend L_1\hbox{-}L_2 SVM for regression analysis with automatic feature selection. We further improve the L_1\hbox{-}L_2 SVM for prognostic prediction by utilizing the information of censored data as constraints. We design an efficient solution to the new optimization problem. The proposed method is compared with other seven prognostic prediction methods on three real-world data sets. The experimental results show that the proposed method performs consistently better than the medium performance. It is more efficient than other algorithms with the similar performance.