Active Learning as a Way of Increasing Accuracy
Hamza Osman İlhan, Mehmet Fatih Amasyalı · International Journal of Computer Theory and Engineering · 2014
Abstract—In machine-learning areas, number of the data for training process alters the success of models. More samples in training give more success. However obtaining data with label information is costly and long-lasting process. Active learning algorithms are emerged to overcome this problem. It can be used with any machine learning algorithms. Active learning algorithms try to maintain same success resulted by regular machine learning methods with fewer samples. In this study, a modified active learning algorithm tested on six datasets with different machine learning methods. Comparative results presented with charts in result. Algorithm are not only providing same success but also slightly increasing total success with smarter training process.