Browsing by Author "Birant K.U."

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    Development of an interactive game-based learning environment to teach data mining
    (Tempus Publications, 2017) Cengiz M.; Birant K.U.; Yildirim P.; Birant D.
    Game-based learning has become a popular topic in all levels of education. A number of computer games have been developed to teach different subjects such as mathematics, English language, medicine, and music. This paper presents the first study that proposes the development of edutainment games to teach data mining techniques with the scope of gamebased learning. The aim ofthis study is to provide an environment that is both fun and enables the achievementof learning goals in data mining training in computer engineering. An escape game called Mine4Escape, which consists of different rooms to teach different data mining techniques (classification and association rule mining), has been developed for individuals at the undergraduate and post-graduate levels. The advantages of the proposed approach are discussed in comparison with traditional data mining training. In addition, this paper describes a dynamic scoring system designed for game-based learning. Finally, an experimental study was carried out to evaluate the performance of our learning environment by analyzing feedback received from a test group consisting of 39 undergraduate and graduate students in computer engineering. Thefindings from thequestionnaire show that it is possible to enhance knowledge acquisition about data mining via the game-based approach. However, the degree of learning interest and information acceptance changes according to students' age, gender, educational level, and game habits. © 2017 TEMPUS Publications.
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    Comparative analysis of ensemble learning methods for signal classification; [Sinyal siniflandirmasi için topluluk öǧrenmesi yöntemlerinin karşilaştirmali analizi]
    (Institute of Electrical and Electronics Engineers Inc., 2018) Yildirim P.; Birant K.U.; Radevski V.; Kut A.; Birant D.
    In recent years, the machine learning algorithms commenced to be used widely in signal classification area as well as many other areas. Ensemble learning has become one of the most popular Machine Learning approaches due to the high classification performance it provides. In this study, the application of four fundamental ensemble learning methods (Bagging, Boosting, Stacking, and Voting) with five different classification algorithms (Neural Network, Support Vector Machines, k-Nearest Neighbor, Naive Bayes, and C4.5) with the most optimal parameter values on signal datasets is presented. In the experimental studies, ensemble learning methods were applied on 14 different signal datasets and the results were compared in terms of classification accuracy rates. According to the results, the best classification performance was obtained with the Random Forest algorithm which is a Bagging based method. © 2018 IEEE.
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    Comparison of Ensemble-Based Multiple Instance Learning Approaches
    (Institute of Electrical and Electronics Engineers Inc., 2019) Taser P.Y.; Birant K.U.; Birant D.
    Multiple instance learning (MIL) is concerned with learning from training set of bags including multiple feature vectors. This paradigm has various algorithms as a solution for multiple instance problem. Recently, ensemble learning has become one of the most preferred machine learning technique because its high classification ability. The main goal of ensemble learning is combining multiple learning models and obtaining a decision from all outputs of these models. Considering this motivation, the study presented in this paper proposes an ensemble-based multiple instance learning approach which merges standard algorithms (MIWrapper and SimpleMI) with ensemble learning methods (Bagging and AdaBoost) to improve classification ability. The proposed approach includes ensemble of combination of MIWrapper and SimpleMI learners with Naive Bayes, Support Vector Machines (SVM), Neural Networks (Multilayer Perceptron (MLP)), and Decision Tree (C4.5) as base classifiers. In the experimental studies, the proposed ensemble-based approach was compared with individual MIWrapper and SimpleMI algorithms in terms of accuracy. The obtained results indicate that the ensemble-based approach shows higher classification ability than the conventional solutions. © 2019 IEEE.