Bayessian Clasiffier Supported By Coverage And Acuracy In Breast Cancer Detection
This article shows the importance of Bayesian classifiers for prediction in data mining, also as important components such as coverage and accuracy may improve the classification performance in themselves an analysis by performing a mathematical model such as Naive Bayes can be improved by adding coverage and precision. Finally, we believe that this improvement may be useful in many types of applications, so this application can serve as a support tool for research on breast cancer and as a decision making in the allocation of resources for prevention and treatment, also can also be used in previous applications to be improved in many ways.
Nieto B. Wilson Ni(n)o Ruiz Elias David Ricardo elizzola
Departamento de Ing. De Sistemas Universidad del Norte-Colombia
国际会议
桂林
英文
98-101
2010-11-17（万方平台首次上网日期，不代表论文的发表时间）