Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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Subject: search.filters.subject.Adaptive Boosting

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  • Conference Object
    Citation - Scopus: 5
    Identifying Taxonomic Biomarkers of Colorectal Cancer in Human Intestinal Microbiota Using Multiple Feature Selection Methods
    (Institute of Electrical and Electronics Engineers Inc., 2022-09-07) Jabeer, Amhar; Kocak, Aysegul; Akkaş, Huseyin; Yenisert, Ferhan; Nalbantoĝlu, Özkan Ufuk; Yousef, Malik; Bakir-Güngör, Burcu; Bakir Gungor, Burcu
    A variety of bacterial species called gut microbiota work together to maintain a steady intestinal environment. The gastrointestinal tract contains tremendous amount of different species including archaea, bacteria, fungi, and viruses. While these organisms are crucial immune system stabilizers, the dysbiosis of the intestinal flora has been related to gastrointestinal disorders including Colorectal cancer (CRC), intestinal cancer, irritable bowel syndrome and inflammatory bowel disease. In the last decade, next-generation sequencing (NGS) methods have accelerated the identification of human gut flora. CRC is a deathly condition that has been on the rise in the last century, affecting half a million people each year. Since early CRC diagnosis is critical for an effective treatment, there is an immediate requirement for a classification system that can expedite CRC diagnosis. In this study, via analyzing the available metagenomics data on CRC, we aim to facilitate the CRC diagnosis via finding biomarkers linked with CRC, and via building a classification model. We have obtained the metagenomic sequencing data of the healthy individuals and CRC patients from a metagenome-wide association analysis and we have classified this data according to the disease stages. Conditional Mutual Information Maximization (CMIM), Fast Correlation Based Filter (FCBF), Extreme Gradient Boosting (XGBoost), min redundancy max relevance (mRMR), Information Gain (IG) and Select K Best (SKB) feature selection algorithms were utilized to cope with the complexity of the features. We observed that the SKB, IG, and XGBoost techniques made significant contributions to decrease the microbiota in use for CRC diagnosis, thereby reducing cost and time. We realized that our Random Forest classifier outperformed Adaboost, Support Vector Machine, Decision Tree, Logitboost and stacking ensemble classifiers in terms of CRC classification performance. Our results reiterated some known and some potential microbiome associated mechanisms in CRC, which could aid the design of new diagnostics based on the microbiome. © 2022 Elsevier B.V., All rights reserved.
  • Conference Object
    Citation - Scopus: 21
    Assessing Employee Attrition Using Classifications Algorithms
    (Association for Computing Machinery, 2020-05-15) Ozdemir, Fatma; Cos¸kun, Mustafa; Gezer, Cengiz; Güngör, Vehbi Çağrı; Coskun, Mustafa; Cagri Gungor, V.
    Employees leave an organization when other organizations offer better opportunities than their current organizations. Continuity and sustenance and even completion of jobs are crucial issues for the companies not to suffer financial losses. Especially if the talented employees, who are at critical positions in the companies, leave the job, it becomes difficult for the organizations to maintain their businesses. Today, organizations would like to predict attrition of their employees and plan and prepare for it. However, the HR departments of organizations are not advanced enough to make such predictions in a handcrafted manner. For this reason, organizations are looking for new systems or methods that automatize the prediction of employee attrition utilizing data mining methods. In this study, we use IBM HR data set and apply different classification methods, such as Support Vector Machine (SVM), Random Forest, J48, LogitBoost, Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Naive Bayes, Bagging, AdaBoost, Logistic Regression, to predict the employee attrition. Different from exiting studies, we systematically evaluate our findings with various classification metrics, such as F-measure, Area Under Curve, accuracy, sensitivity, and specificity. We observe that data mining methods can be useful for predicting the employee attrition. © 2022 Elsevier B.V., All rights reserved.
  • Conference Object
    Citation - Scopus: 2
    ATGRUVAE: Reducing Noise and Improving Forecasting Performance in Stock Data
    (Institute of Electrical and Electronics Engineers Inc., 2024-10-26) Akkaş, Huseyin; Kolukisa, Burak; Bakir-Güngör, Burcu
    Nowadays, to maximize their income, investors and researchers try to predict the future prices of stocks in the market using artificial intelligence algorithms. However, noise in stock price fluctuations negatively a ffects t he accuracy of the forecasts. To this end, Attention Based Variational Autoencoders with Gated Recurrent Units (ATGRUVAE) method is developed to remove the noise in stock price fluctuations a nd compared with variational, basic and noise removing autoencoders. Exper-iments are conducted using historical stock prices of well-known companies such as Apple, Google and Amazon and 9 different indicator values derived from these stock prices. The noise cleaned stocks are then trained and tested on Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM) and Linear Regression (LR) models. The results show that the proposed ATGRUVAE model outperforms all three models and demonstrates its ability to capture complex patterns in stock market data. © 2025 Elsevier B.V., All rights reserved.
  • Conference Object
    Population Specific Classification of Colorectal Cancer With Meta-Analysis of Metagenomic Data
    (Institute of Electrical and Electronics Engineers Inc., 2023-10-11) Temiz, Mustafa; Yousef, Malik; Bakir-Güngör, Burcu
    Advances in next-generation sequencing and '-omics' technologies makes it possible to characterize the human gut microbiome. While some of these microorganisms are important regulators of our immune system, modulation of the microbiota leads to a variety of diseases. Colorectal cancer (CRC), the third most common cancer worldwide, is caused by genetic mutations, environmental conditions, and abnormalities in the gut microbiota. Using various machine learning methods and meta-analysis techniques, this study aims to build a classification model that can help in CRC diagnosis by analyzing metagenomic datasets of different populations obtained at the species level. Using 8 different countries and 9 different metagenomic datasets, 3 different meta-analyzes are performed: within-population, cross-population, and one population is selected for testing and the rest is used as a training dataset (LODO). For CRC classification, 4 different classification algorithms (Random Forest (RF), Logitboost, Adaboost, and Decision Tree (DT)) are used. The best performance among these methods was obtained with the Random Forest algorithm with an AUC of 0.98 by using JP for the training data set and JPN populations for the test data set in the cross-population performance evaluation. © 2023 Elsevier B.V., All rights reserved.
  • Conference Object
    Citation - Scopus: 1
    Man-Hour Prediction for Complex Industrial Products
    (Institute of Electrical and Electronics Engineers Inc., 2023) Unal, Ahmet Emin; Boyar, Halit; Kuleli Pak, Burcu Kuleli; Cem Yildiz, Mehmet; Erten, Ali Erman; Güngör, Vehbi Çağrı; Pak, Burcu Kuleli; Cagri Gungor, Vehbi
    Accurately predicting the cost is crucial for the success of complex industrial projects. There can be several sources contributing to the cost. Traditional methods for cost estimation may not provide the required accuracy and speed to ensure the success of the project. Recently, machine learning techniques have shown promising results in improving cost estimation in various industrial products. This study investigates the performance of gradient-boosting machine learning models and feature engineering techniques on a private dataset of metal sheet project man-hour costs. A comparison of distinct models is conducted, key aspects influencing cost are identified, and the implications of incorporating domain-specific knowledge, including its advantages and disadvantages, are assessed based on performance outcomes. Experimental results demonstrate that LightGBM and XGBoost outperform other models, and feature selection and synthetic data generation techniques improve the performance. Overall, this study highlights the potential of machine learning in metal sheet sampling projects and emphasizes the importance of feature engineering and domain expertise for better model performance. © 2024 Elsevier B.V., All rights reserved.