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General Information
    • ISSN: 1793-8201 (Print)
    • Abbreviated Title: Int. J. Comput. Theory Eng.
    • Frequency: Quarterly
    • DOI: 10.7763/IJCTE
    • Editor-in-Chief: Prof. Mehmet Sahinoglu
    • Associate Editor-in-Chief: Assoc. Prof. Alberto Arteta
    • Executive Editor: Ms. Mia Hu
    • Abstracting/Indexing: Scopus (Since 2022), INSPEC (IET), CNKI,  Google Scholar, EBSCO, etc.
    • E-mail: ijcte@iacsitp.com
Prof. Mehmet Sahinoglu
Computer Science Department, Troy University, USA
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.

IJCTE 2014 Vol.6(6): 460-465 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2014.V6.910

Active Learning as a Way of Increasing Accuracy

Hamza Osman Ilhan and Mehmet Fatih Amasyalı

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).


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.

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