Low false positive learning with support vector machines |
| |
| Affiliation: | 1. Department of Mathematics and Physics, North China Electric Power University, China;2. School of Science, Communication University of China, China;1. School of Information and Electronics, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, PR China;2. Department of Electronic Engineering, Chung Yuan Christian University, No. 200, Zhongbei Rd., Zhongli City, Taoyuan County 320, Taiwan, ROC;1. Department of Biomedical Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing, Jiangsu 211106, China;2. Department of Radiology, Guangdong Province Traditional Chinese Medical Hospital, Guangzhou 510006, China;1. College of Information Science and Technology, Beijing Normal University, Beijing, China;2. State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China;3. Banner Alzheimer’s Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA |
| |
| Abstract: | Most machine learning systems for binary classification are trained using algorithms that maximize the accuracy and assume that false positives and false negatives are equally bad. However, in many applications, these two types of errors may have very different costs. In this paper, we consider the problem of controlling the false positive rate on SVMs, since its traditional formulation does not offer such assurance. To solve this problem, we define a feature space sensitive area, where the probability of having false positives is higher, and use a second classifier (unanimity k-NN) in this area to better filter errors and improve the decision-making process. We call this method Risk Area SVM (RA-SVM). We compare the RA-SVM to other state-of-the-art methods for low false positive classification using 33 standard datasets in the literature. The solution we propose shows better performance in the vast majority of the cases using the standard Neyman–Pearson measure. |
| |
| Keywords: | Support vector machines Low false positive learning |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
