Dalgacik K-en Yakin Komşuluk Yöntemi̇ İle Hava Ki̇rli̇li̇ği̇ Tahmi̇ni̇

Altunkaynak, Abdüsselam; Başakın, Eyyup Ensar; Kartal, Elif

doi:10.17482/uumfd.809938

Cited by 12 publications

(7 citation statements)

References 15 publications

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“…An increase in the K value increases the fit to the test data. In Multiple Linear Regression problems based on prediction, the dependent variable is calculated by taking the arithmetic mean of the independent variables as much as the optimum k value of the independent variables (Altunkaynak, Başakın & Kartal, 2020).…”

Section: K-nearest Neighbours Algorithmmentioning

confidence: 99%

Data mining application with machine learning algorithms to manage interest rate risk

KOÇOĞLU

Ersöz²

2022

bmij

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In trade, the risks taken may increase the expected income; however, they may also cause large amounts of losses as well. Banks transfer the capital and the deposits they collect from their clients to the individuals or institutions in need of profit, taking certain risks into account. One of the important risks taken in this process of capital transfer is the market's change in interest or profit share rates. If the bank transfers the deposit collected with a certain commitment to the market at a lower rate, it will make a loss. Models for predicting future interest or profit share rates gain importance for preventing this situation. The aim of this study is to determine which variables will be taken into account for the loan interest rate that banks will offer to their customers during the lending process, and to create a machine learning model that can predict the loan interest rate that the bank will offer to its customers by using these variables. Multiple Linear Regression analysis was performed to demonstrate the relationship between the variables selected based on the literature review, expert opinions, and the interest rate. In order to facilitate decision-makers in practice, Random Forests, Decision Trees, K-Nearest Neighbours (KNN), Artificial Neural Networks (ANN), and Support Vector Machine (SVM) algorithms from machine learning algorithms were compared by using the prediction model. Accuracy Rate, Cohen's Kappa, Precision, Sensitivity, and F-Measure measurements were used to compare the algorithms used in the study. According to the analysis results, it was observed that the Random Forest algorithm was more successful on the first model consisting of weekly data. The Decision Tree algorithm succeeded more on the second model consisting of monthly data prediction performance. In the model consisting of weekly data, USD Selling Price, Stock Index (BIST100), and Central Bank Gold Reserve from the Multiple Linear Regression variables were found significant in affecting the interest rate.

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Section: K-nearest Neighbours Algorithmmentioning

confidence: 99%

Data mining application with machine learning algorithms to manage interest rate risk

KOÇOĞLU

Ersöz²

2022

bmij

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“…Öklid mesafesi, 2 boyutlu çözüm kümesi örneğinde x düzlemindeki doğrusal uzaklık 𝑥𝑖'ye ve y düzlemindeki doğrusal uzaklık 𝑦𝑖'ye bağlı olarak bulunabilir [10].…”

Section: Eksik Verilerin Tamamlanmasıunclassified

Detection of Mammographic Mass Class with Machine Learning Techniques

ERİŞ

Bozkurt

2022

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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Anahtar kelimeler: GINI algoritması, Karar ağaçları, Yapay sinir ağları, İleri beslemeli geri yayılımlı ağ, Mamografi, Hata matrisi. ÖzMeme kanseri, dünyada kadınlar arasında ölüme neden olabilen kanser tiplerinin en sık görülenlerinden biridir. Günümüzde meme kanseri teşhisinde farklı görüntüleme yöntemleri kullanılmakla birlikte bu yöntemler zaman zaman gereksiz biyopsiye yönlendirebilmektedir. Bu çalışmada, makine öğrenmesi tekniklerinden, karar ağaçları ve yapay sinir ağları yöntemleri kullanılarak mamografik kitlenin sınıfı, hastaya ve kitleye ait özelliklerin değerinden tespit edilmiştir. Karar ağaçlarında, GINI algoritması kullanılmış ve RapidMiner programından yararlanılmıştır. Yapay sinir ağlarında, ileri beslemeli geri yayılımlı ağ modeli MATLAB'de yazılan program aracılığı ile kullanılmıştır. Bu çalışmada kullanılan veri seti, Erlangen-Nuremberg Üniversitesi, Radyoloji Enstitüsü, Jinekolojik Radyoloji bölümünden elde edilen 961 örnekten oluşmaktadır. Her örnek için 5 adet özellik mevcuttur. Bu özellikler, BI-RADS (meme görüntüleme raporlama ve veri sistemleri) değerlendirmesi, yaş, kitle şekli, kitlenin kenar boşluğu ve kitlenin yoğunluğu bilgilerini içermektedir. Yaklaşımımız, mamografide tespit edilen kitleleri iyi huylu ve kötü huylu olmak üzere iki farklı sınıfa ayırmaktadır. Her iki yöntemin performanslarının karşılaştırılması, doğruluk, duyarlılık, kesinlik, seçicilik, F-skor ve AUC (eğri altındaki alan) değerlerine bakılarak yapılmıştır.

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“…In this way, it is aimed to improve the model performance. In addition, the most widely used Euclidean distance criterion for KNN was used in the study (Altunkaynak et al, 2020).…”

Section: K-nearest Neighbors (Knn)mentioning

confidence: 99%

Random Forest ve K-Nearest Neighbor Yöntemleri ile Günlük Akım Modellemesi

Dalkılıç

YEŞİLYURT

SAMUİ³

2021

Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Water is an indispensable natural resource for life. Therefore, protection and control of water resources are of great importance. Since river flow estimation and modeling are very important in cases such as the management of water resources, irrigation, it is included in the literature as an issue that needs constant research and development. A large number of techniques are being used for estimation and modeling; thus, the estimation results are gradually improving with the development of the studies carried out, the comparison of techniques, and the determination and removal of the shortcomings. In this study, Random Forest and K-Nearest Neighbors nonlinear regression models, which are two of the machine learning methods, were used to evaluating the estimation results, to find the better estimation method, and to determine the advantages and disadvantages of these methods. In addition, Random Search and Grid Search methods were used to make the hyperparameter selection and comparison for the Random Forest model. In this study, in which daily flow data of 1981-2011 of the two stations in the Euphrates were used, and, when compared to other models, it was observed that better results were obtained when Random Search was applied to determine the hyperparameters of the Random Forest model.

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Dalgacik K-en Yakin Komşuluk Yöntemi̇ İle Hava Ki̇rli̇li̇ği̇ Tahmi̇ni̇

Cited by 12 publications

References 15 publications

Data mining application with machine learning algorithms to manage interest rate risk

Data mining application with machine learning algorithms to manage interest rate risk

Detection of Mammographic Mass Class with Machine Learning Techniques

Random Forest ve K-Nearest Neighbor Yöntemleri ile Günlük Akım Modellemesi

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