Figure 2.

Multiclass classification scheme. The multiclass cancer classification problem is divided into a series of 14 OVA problems, and each OVA problem is addressed by a different class-specific classifier (e.g., “breast cancer” vs. “not breast cancer”). Each classifier uses the SVM algorithm to define a hyperplane that best separates training samples into two classes. In the example shown, a test sample is sequentially presented to each of 14 OVA classifiers and is predicted to be breast cancer, based on the breast OVA classifier having the highest confidence.