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.
Index Terms—Active learning, random forest, single vector
machines, k-nearest neighbor, naïve bayes, machine learning.
The authors are with Yıldız Technical University, Istanbul, 34220, TR
(e-mail: hoilhan@yildiz.edu.tr, mfatih@ce.yildiz.edu.tr).
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Cite:Hamza Osman Ilhan and Mehmet Fatih Amasyalı, "Active Learning as a Way of Increasing Accuracy," International Journal of Computer Theory and Engineering vol. 6, no. 6, pp. 460-465, 2014.
