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Start date: 2010End date: 2019Type: search.filters.itemtype.Conference PaperSubject: search.filters.subject.Feature ExtractionSubject: search.filters.subject.Feature SelectionSubject: search.filters.subject.Artificial Neural Network

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    Enhanced feature selection from wavelet packet coefficients in fault diagnosis of induction motors with artificial neural networks
    (2010) Eren, Levent; Cekic, Yalcin; Devaney, Michael Joseph; Eren, Levent, College of Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Cekic, Yalcin, College of Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Devaney, Michael Joseph, College of Engineering, Columbia, United States
    Wavelet packet decomposition (WPD) of line current has been successfully applied in motor fault detection. Enhanced feature selection from wavelet packet coefficients (WPCs) is presented in this paper. This method involves the decomposition of motor current into equally spaced frequency bands by using an all-pass implementation of elliptic IIR half-band filters in the filter bank structure to obtain WPCs in a computationally efficient way. Then, the bias in WPCs for each frequency band is removed to suppress both power system harmonics and leakage from adjacent frequency bands. Finally, the enhanced features are used as inputs to an ANN to provide motor fault detection with higher fault detection rate. © 2010 IEEE. © 2010 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.