CEMALETTİN KUBAT

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I am a professor at Dept. of Industrial Engineering_İstanbul Gelisim University, I am working on Artifical Intelligent, Optimization, Dijital Techhologies, Problem Solutions with MATLAB , I am giving some courses at Industrial Engineering, Sortware Engineering, aeronautical engineering. I am organizing IMSS Symposium Series with our team at Sakarya University and Sakarya Applied Science University.

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Andrej Dujella

University of Zagreb

Hemin Koyi

Uppsala University

Jana Javornik

University of East London

Graham Martin

University of Leicester

Gwen Robbins Schug

University of North Carolina at Greensboro

Gabriel Gutierrez-Alonso

University of Salamanca

John Sutton

Macquarie University

Eros Carvalho

Universidade Federal do Rio Grande do Sul

Kevin Arbuckle

Swansea University

Jesper Hoffmeyer

University of Copenhagen

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Papers by CEMALETTİN KUBAT

Application of Chi-square discretization algorithms to ensemble classification methods

Expert Systems with Applications

Abstract Classification is one of the important tasks in data mining and machine learning. Classi... more Abstract Classification is one of the important tasks in data mining and machine learning. Classification performance depends on many factors as well as data characteristics. Some algorithms are known to work better with discrete data. In contrast, most real-world data contain continuous variables. For algorithms working with discrete data, these continuous variables must be converted to discrete ones. In this process called discretization, continuous variables are converted to their corresponding discrete variables. In this paper, four Chi-square based supervised discretization algorithms ChiMerge(ChiM), Chi2, Extended Chi2(ExtChi2) and Modified Chi2(ModChi2) were used. In the literature, the performance of these algorithms is often tested with decision trees and Naive Bayes classifiers. In this study, differently, four sets of data discretized by these algorithms were classified with ensemble methods. Classification accuracies for these data sets were obtained through using a stratified 10-fold cross-validation method. The classification performance of the original and discrete data sets of the methods is presented comparatively. According to the results, the performance of the discrete data is more successful than the original data.

A Hybrid Modified Deep Learning Data Imputation Method for Numeric Datasets

International Journal of Intelligent Systems and Applications in Engineering, 2021

Missing data is a major problem in terms of both machine learning and data mining methods. Like m... more Missing data is a major problem in terms of both machine learning and data mining methods. Like most of these methods do not work with missing data, negative results may occur on the performance of the working ones, also. Imputation is a data preprocessing method used to replace missing data with appropriate values. This study aims at developing a hybrid modified imputation method based on deep learning approach. For this purpose, we use Random Forest and Datawig deep learning imputation (called RF-DLI) methods together. Datawig is a deep learning-based library that supports missing value imputation for all types of data. RF-DLI approach includes the following steps to impute missing data. First, the importance of each attribute of the data set is determined with Random Forest (RF). Second, the most important 50% of the attributes are selected. Finally, each missing value is imputed with datawig (DLI) using these most important attributes. The study uses six real-world data sets from different fields with 30% missing data. The imputation performance of RF-DLI is compared to K-Nearest Neighbors (KNN), Multiple Imputation by Chained Equations (MICE), MEAN imputation, and Principle Component Analysis (PCA) imputation approaches in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-square (R 2 ) evaluation metrics. The results show that in most cases, the RF-DLI approach has better imputation performance than the other techniques mentioned.

CEMALETTİN KUBAT

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