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Start date: 2010End date: 2019Type: search.filters.itemtype.Conference PaperSubject: search.filters.subject.Feature ExtractionSubject: search.filters.subject.Feature SelectionAuthor: search.filters.author.Erdogdu Sakar, Betul

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    Prediction of level and abrupt changes of ozon concentration, Ozon seviyesi ve ani deǧişimlerinin kestirimi
    (IEEE Computer Society [email protected], 2014) Develi, Ahmet; Kursun, Olcay; Erdogdu Sakar, Betul; Develi, Ahmet, Istanbul Üniversitesi, Istanbul, Turkey; Kursun, Olcay, Istanbul Üniversitesi, Istanbul, Turkey; Erdogdu Sakar, Betul, Bahçeşehir Üniversitesi, Istanbul, Turkey
    While, in stratosphere, high level ozone concentration protects the Earth against ultraviolet radiation, in lower troposphere it has negative effects on human health and environment. The goal of this study is to determine the feature groups that are related to abrupt changes in the level of ozone. Linear discriminant analysis and support vector machines methods are used to explore which combination of features are predictive of abrupt changes in ozone level on the simulation dataset collected in Ankara, Turkey, by an automatic air quality monitoring station operated by the ministry of environment and urban planning. The dataset consists of one year of measurements of air pollutants and the meteorological factors. The obtained results showed that particulate matters, nitric oxides and temperature are most effective parameters in the classification of absurt rise and fall in the level of ozone. © 2014 IEEE. © 2014 Elsevier B.V., All rights reserved.
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    Variable Importance Analysis in Default Prediction using Machine Learning Techniques
    (SciTePress, 2018) Gültekin, Basak; Erdogdu Sakar, Betul; Bernardino, J.; Quix, C.; Gültekin, Basak, Faculty of Engineering and Natural Sciences, Bahçeşehir Üniversitesi, Istanbul, Turkey; Erdogdu Sakar, Betul, Faculty of Engineering and Natural Sciences, Bahçeşehir Üniversitesi, Istanbul, Turkey
    In this study, different data mining techniques were applied to a finance credit data set from a financial institution to provide an automated and objective profitability measurement. Two-step methodology was used Determining the variables to be included in the model and deciding on the model to classify the potential credit application as bad credit (default) or good credit (not default). The phrases bad credit and good credit are used as class labels since they are used like this in financial sector jargon in Turkey. For this twostep procedure, different variable selection algorithms like Random Forest, Boruta and machine learning algorithms like Logistic Regression, Random Forest, Artificial Neural Network were tried. At the end of the feature selection phase, CRA and III variables were determined as most important variables. Moreover, occupation and product number were also predictor variables. For the classification phase, Neural Network model was the best model with higher accuracy and low average square error also Random Forest model better resulted than Logistic Regression model. © 2020 Elsevier B.V., All rights reserved.