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
Smoothing methods for convex inequalities and linear complementarity problems
Mathematical Programming: Series A and B
A class of smoothing functions for nonlinear and mixed complementarity problems
Computational Optimization and Applications
SSVM: A Smooth Support Vector Machine for Classification
Computational Optimization and Applications
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Successive overrelaxation for support vector machines
IEEE Transactions on Neural Networks
Knowledge-Based Kernel Approximation
The Journal of Machine Learning Research
Expert Systems with Applications: An International Journal
Knowledge based Least Squares Twin support vector machines
Information Sciences: an International Journal
A Software Tool for Determination of Breast Cancer Treatment Methods Using Data Mining Approach
Journal of Medical Systems
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
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The identification of breast cancer patients for whom chemotherapy could prolong survival time is treated here as a data mining problem. This identification is achieved by clustering 253 breast cancer patients into three prognostic groups: Good, Poor and Intermediate. Each of the three groups has a significantly distinct Kaplan-Meier survival curve. Of particular significance is the Intermediate group, because patients with chemotherapy in this group do better than those without chemotherapy in the same group. This is the reverse case to that of the overall population of 253 patients for which patients undergoing chemotherapy have worse survival than those who do not. We also prescribe a procedure that utilizes three nonlinear smooth support vector machines (SSVMs) for classifying breast cancer patients into the three above prognostic groups. These results suggest that the patients in the Good group should not receive chemotherapy while those in the Intermediate group should receive chemotherapy based on our survival curve analysis. To our knowledge this is the first instance of a classifiable group of breast cancer patients for which chemotherapy can possibly enhance survival.