An AUC-based Active Learning Algorithm via LogitBoost for Binary Classification
| Autor: | Zhe-Bin Zhang, 張哲彬 |
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| Rok vydání: | 2018 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 106 Because obtaining complete labeled data is quite expensively, we proposed an active learning algorithm to solve this problem. The proposed active learning algorithm includes two major parts. First, we use the AUC (area under ROC curve) as criteria to select new unlabeled sample, which will be added into the training set, and then the classifier will be re-trained in the next step. Second, we use the LogitBoost algorithm as the base classifier and modifie the weighs based on AUC resulting form considering predictive power as the goal. Moreover, when the data are large, it will take too much time to search all unlabeled samples and to find the most contributive one. Therefore, we use cluster analysis to reduce the samples firstly, then the proposed active learning algorithm is applied. The simulation results present that the proposed algorithm uses fewer samples and still can provide acceptable predictive performance. Three real applications were also used to evaluate the performance of the proposed method. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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