Pool-based learning via Weighted Information Gain Measurements

1. Pool-based learning via Weighted Information Gain Measurements Rafael Augusto Ferreira do Carmo carmorafael@gmail.com Daniel Pinto Coutinho daniel.pintocoutinho@gmail.com Jerffeson Teixeira de Souza jeff@larces.uece.br Universidade Estadual do Ceará Fortaleza - Brazil

2. Introduction • Active learning scenario • Binary classification problems • Pool of unlabeled examples • No prior information about class distribution • One labeled example as “seed” for learning

3. The Task • Select the as few “informative examples” as possible • Minimize the classification costs • Maximize the quality of the model

4. The Algorithm • What if this example is positive? • What if this example is negative? • Information Gain Ratio • Weight features

5. The Algorithm

6. Results – Datasets

7. Results - Ranking