Researcher:
Manukyan, Artur

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PhD Student

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Artur

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Manukyan

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Manukyan, Artur

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2014 - 20163

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Researcher Of Publications: search.filters.isAuthorOfPublication.209acfd2-b235-4895-bb1f-9ebd77d6fe0d

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    Classification of imbalanced data with a geometric digraph family
    (Microtome Publ, 2016) Ceyhan, Elvan; N/A; Manukyan, Artur; PhD Student; Graduate School of Sciences and Engineering; N/A
    We use a geometric digraph family called class cover catch digraphs (CCCDs) to tackle the class imbalance problem in statistical classification. CCCDs provide graph theoretic solutions to the class cover problem and have been employed in classification. We assess the classification performance of CCCD classifiers by extensive Monte Carlo simulations, comparing them with other classifiers commonly used in the literature. In particular, we show that CCCD classifiers perform relatively well when one class is more frequent than the other in a two-class setting, an example of the cl ass imbalance problem. We also point out the relationship between class imbalance and class overlapping problems, and their influence on the performance of CCCD classifiers and other classification methods as well as some state-of-the-art algorithms which are robust to class imbalance by construction. Experiments on both simulated and real data sets indicate that CCCD classifiers are robust to the class imbalance problem. CCCDs substantially undersample from the majority class while preserving the information on the discarded points during the undersampling process. Many state-of-the-art methods, however, keep this information by means of ensemble classifiers, but CCCDs yield only a single classifier with the same property, making it both appealing and fast.
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    Dandelion plot: a method for the visualization of R-mode exploratory factor analyses
    (Springer Heidelberg, 2014) Çene, Erhan; Sedef, Ahmet; Demir, İbrahim; N/A; Manukyan, Artur; PhD Student; Graduate School of Sciences and Engineering; N/A
    One of the important aspects of exploratory factor analysis (EFA) is to discover underlying structures in real life problems. Especially, R-mode methods of EFA aim to investigate the relationship between variables. Visualizing an efficient EFA model is as important as obtaining one. A good graph of an EFA should be simple, informative and easy to interpret. A few number of visualization methods exist. Dandelion plot, a novel method of visualization for R-mode EFA, is used in this study, providing a more effective representation of factors. With this method, factor variances and factor loadings can be plotted on a single window. The representation of both positivity and negativity among factor loadings is another strength of the method.
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    PublicationOpen Access
    Classification of imbalanced data with a geometric digraph family
    (Journal of Machine Learning Research (JMLR), 2016) Department of Mathematics; Manukyan, Artur; Ceyhan, Elvan; PhD Student; Undergraduate Student; Faculty Member; Department of Mathematics; Graduate School of Sciences and Engineering; College of Sciences
    We use a geometric digraph family called class cover catch digraphs (CCCDs) to tackle the class imbalance problem in statistical classification. CCCDs provide graph theoretic solutions to the class cover problem and have been employed in classification. We assess the classification performance of CCCD classifiers by extensive Monte Carlo simulations, comparing them with other classifiers commonly used in the literature. In particular, we show that CCCD classifiers perform relatively well when one class is more frequent than the other in a two-class setting, an example of the cl ass imbalance problem. We also point out the relationship between class imbalance and class overlapping problems, and their influence on the performance of CCCD classifiers and other classification methods as well as some state-of-the-art algorithms which are robust to class imbalance by construction. Experiments on both simulated and real data sets indicate that CCCD classifiers are robust to the class imbalance problem. CCCDs substantially undersample from the majority class while preserving the information on the discarded points during the undersampling process. Many state-of-the-art methods, however, keep this information by means of ensemble classifiers, but CCCDs yield only a single classifier with the same property, making it both appealing and fast.