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All Journal International Journal of Intelligent Systems and Applications in Engineering
SABANCI, Kadir
Prof. Dr. Ismail SARITAS
Computer Science & IT

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Classification of Heuristic Information by Using Machine Learning Algorithms KOKLU, Murat; SABANCI, Kadir; UNLERSEN, Muhammed Fahri
International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2016Special Issue-146984

Abstract

The User Knowledge Modelling dataset in the UCI machine learning repository was used in this study. The students were classified into 4 class (very low, low, middle, and high) due to the 5 performance data in the dataset. 258 data of 403 data in the dataset were used for training and 145 of them were used for tests. The Weka (Waikato Environment for Knowledge Analysis) software was used for classification. In classification Multilayer Perceptron (MLP), k Nearest Neighbors (kNN), J48, NativeBayes, BayesNet, KStar, RBFNetwork and RBFClassifier machine learning algorithms were used and success rates and error rates were calculated. In this study 8 different data mining algorithm were used and the best classification success rate was obtained by MLP. With Multilayer perceptron neural network model the classification success rates was calculated when there are different number of neurons in the hidden layer of MLP. The best classification success rate was achieved as 97.2414% when there was 8 neurons in the hidden layer. MAE and RMSE values were obtained for this classification success rate as 0.0242 and 0.1094 respectively.
Breast Cancer Diagnosis by Different Machine Learning Methods Using Blood Analysis Data Aslan, Muhammet Fatih; Celik, Yunus; Sabanci, Kadir; Durdu, Akif
International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 4 (2018)
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2018648455

Abstract

Today, one of the most common types of cancer is breast cancer. It is crucial to prevent the propagation of malign cells to reduce the rate of cancer induced mortality. Cancer detection must be done as early as possible for this purpose. Machine Learning techniques are used to diagnose or predict the success of treatment in medicine. In this study, four different machine learning algorithms were used to early detection of breast cancer. The aim of this study is to process the results of routine blood analysis with different ML methods and to understand how effective this method is for detection. Methods used can be listed as Artificial Neural Network (ANN), standard Extreme Learning Machine (ELM), Support Vector Machine (SVM) and K-Nearest Neighbor (k-NN). Dataset used were taken from UCI library. In this dataset age, body mass index (BMI), glucose, insulin, homeostasis model assessment (HOMA), leptin, adiponectin, resistin and chemokine monocyte chemoattractant protein 1 (MCP1)   attributes were used. Parameters that have the best accuracy values were found by using four different Machine Learning techniques. For this purpose, hyperparameter optimization method was used. In the end, the results were compared and discussed.
Estimation of Credit Card Customers Payment Status by Using kNN and MLP KOKLU, Murat; SABANCI, Kadir
International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2016Special Issue-146983

Abstract

The Default of Credit Card Clients dataset in the UCI machine learning repository was used in this study.  The credit card customers were classified if they would do payment or not (yes=1 no=0) for next month by using 23 information about them. Totally 30000 data in the dataset’s 66% was used for training and rest of them as 33% was used for tests. The Weka (Waikato Environment for Knowledge Analysis) software was used for estimation. In estimation Multilayer Perceptron (MLP) and k Nearest Neighbors (kNN) machine learning algorithms was used and success rates and error rates were calculated. With kNN estimation success rates for various number of neighborhood value was calculated one by one. The highest success rate was achieved as 80.6569% when the number of neighbor is 10. With MLP neural network model the estimation success rates was calculated when there are different number of neurons in the hidden layer of MLP. The best estimation success rate was achieved as 81.049% when there was only one neuron in the hidden layer.  MAE and RMSE values were obtained for this estimation success rate as 0.3237 and 0.388 respectively. 
Co-Authors Aslan, Muhammet Fatih Celik, Yunus Durdu, Akif KOKLU, Murat UNLERSEN, Muhammed Fahri