Abstract
Active learning is becoming more and more important in machine learning which can optimize the learning process. [17] The main concept is that if learning algorithm can choose the decisive data points from which it learns, instead of choosing all of them, it will perform better with less training process. In other words, we aggressively select the unlabeled data instances by observing the known labeled data instances to get the higher accuracy and use smaller amounts of data instances than select all of the dataset or random choose data when training the supervised learning system. [12] For any supervised learning, if you would like to make the system perform well, it had to be trained on lots of labeled instances. But, in these labeled instances, there might be some worthless instances which affect the learning system and raise your training cost. So, we used the active learning concept during training process to discriminate whether the data instance is good for the learning system or not. In this work, we would like to know that the concept of active learning to select the training data, will work or not. In the future, we hope that we can realize a framework which can quantize a parameter to determine which data instance deserve to be labeled through observing exiting dataset. It will refine the dataset and increase the system quality.