Browsing by Author "Birant D."

Now showing 1 - 7 of 7
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    Naive Bayes classifier for continuous variables using novel method (NBC4D) and distributions
    (IEEE Computer Society, 2014) Yildirim P.; Birant D.
    In data mining, when using Naive Bayes classification technique, it is necessary to overcome the problem of how to deal with continuous attributes. Most previous work has solved the problem either by using discretization, normal method or kernel method. This study proposes the usage of different continuous probability distribution techniques for Naive Bayes classification. It explores various probability density functions of distributions. The experimental results show that the proposed probability distributions also classify continuous data with potentially high accuracy. In addition, this paper introduces a novel method, named NBC4D, which offers a new approach for classification by applying different distribution types on different attributes. The results (obtained classification accuracy rates) show that our proposed method (the usage of more than one distribution types) has success on real-world datasets when compared with the usage of only one well known distribution type. © 2014 IEEE.
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    An ant colony optimisation approach for optimising SPARQL queries by reordering triple patterns
    (Elsevier Ltd, 2015) Kalayci E.G.; Kalayci T.E.; Birant D.
    Processing the excessive volumes of information on the Web is an important issue. The Semantic Web paradigm has been proposed as the solution. However, this approach generates several challenges, such as query processing and optimisation. This paper proposes a novel approach for optimising SPARQL queries with different graph shapes. This new method reorders the triple patterns using Ant Colony Optimisation (ACO) algorithms. Reordering the triple patterns is a way of decreasing the execution times of the SPARQL queries. The proposed approach is focused on in-memory models of RDF data, and it optimises the SPARQL queries by means of Ant System, Elitist Ant System and MAX-MIN Ant System algorithms. The approach is implemented in the Apache Jena ARQ query engine, which is used for the experimentation, and the new method is compared with Normal Execution, Jena Reorder Algorithms, and the Stocker et al. Algorithms. All of the experiments are performed using the LUBM dataset for various shapes of queries, such as chain, star, cyclic, and chain-star. The first contribution is the real-time optimisation of SPARQL query triple pattern orders using ACO algorithms, and the second contribution is the concrete implementation for the ARQ query engine, which is a component of the widely used Semantic Web framework Apache Jena. The experiments demonstrate that the proposed method reduces the execution time of the queries significantly. © 2015 Elsevier Ltd. All rights reserved.
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    A framework for data mining and knowledge discovery in cloud computing
    (Springer International Publishing, 2016) Birant D.; Yildirim P.
    The massive amounts of data being generated in the current world of information technology have increased from terabytes to petabytes in volume. The fact that extracting knowledge from large-scale data is a challenging issue creates a great demand for cloud computing because of its potential benefits such as scalable storage and processing services. Considering this motivation, this chapter introduces a novel framework, data mining in cloud computing (DMCC), that allows users to apply classification, clustering, and association rule mining methods on huge amounts of data efficiently by combining data mining, cloud computing, and parallel computing technologies. The chapter discusses the main architectural components, interfaces, features, and advantages of the proposed DMCC framework. This study also compares the running times when data mining algorithms are executed in serial and parallel in a cloud environment through DMCC framework. Experimental results show that DMCC greatly decreases the execution times of data mining algorithms. © Springer International Publishing Switzerland 2016. All rights reserved.
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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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    EBOC: Ensemble-Based Ordinal Classification in Transportation
    (Hindawi Limited, 2019) Yildirim P.; Birant U.K.; Birant D.; Moghaddam M.H.Y.
    Learning the latent patterns of historical data in an efficient way to model the behaviour of a system is a major need for making right decisions. For this purpose, machine learning solution has already begun its promising marks in transportation as well as in many areas such as marketing, finance, education, and health. However, many classification algorithms in the literature assume that the target attribute values in the datasets are unordered, so they lose inherent order between the class values. To overcome the problem, this study proposes a novel ensemble-based ordinal classification (EBOC) approach which suggests bagging and boosting (AdaBoost algorithm) methods as a solution for ordinal classification problem in transportation sector. This article also compares the proposed EBOC approach with ordinal class classifier and traditional tree-based classification algorithms (i.e., C4.5 decision tree, RandomTree, and REPTree) in terms of accuracy. The results indicate that the proposed EBOC approach achieves better classification performance than the conventional solutions. © 2019 Pelin Yildirim et al.
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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.