1. Pool-based learning via Weighted Information Gain Measurements
Rafael Augusto Ferreira do Carmo
Daniel Pinto Coutinho
Jerffeson Teixeira de Souza
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 Dataset Experiment Score (AUC) Global Score (ALC)
verifA 10 B
expB 11 E
expE 14 F
expF 16