WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394

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Publisher: search.filters.publisher.Springer International Publishing AGSubject: search.filters.subject.Machine Learning

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Now showing 1 - 7 of 7
  • Conference Object
    Citation - WoS: 1
    Citation - Scopus: 1
    Textnettopics-SFTS-SBTS Textnettopics Scoring Approaches Based Sequential Forward and Backward
    (Springer International Publishing AG, 2024) Voskergian, Daniel; Bakir-Gungor, Burcu; Yousef, Malik
    TextNetTopics is a text classification-based topic modeling approach that performs topic selection rather than word selection to train a machine learning algorithm. However, one main limitation of TextNetTopics is that its scoring component (the S component) assesses each topic independently and ranks them accordingly, neglecting the potential relationship between topics. In order to address this limitation and improve the classification performance, this study introduces an enhancement to TextNetTopics. TextNetTopics-SFTS-SBTS integrates two novel scoring approaches: Sequential Forward Topic Scoring (SFTS) and Sequential Backward Topic Scoring (SBTS), which consider topic interactions by assessing sets of topics simultaneously. This integration aims to streamline the topic selection process and enhance classifier efficiency for text classification. The results obtained across three datasets offer valuable insights into the context-dependent effectiveness of the new scoring mechanisms across diverse datasets and varying numbers of topics involved in the analysis.
  • Conference Object
    TextNetTopics+: Enhancing Text Classification Through Classifier Diversity and Model Ensembling
    (Springer International Publishing AG, 2025) Voskergian, Daniel; Bakir-Gungor, Burcu; Yousef, Malik
    TextNetTopics is an innovative text classification framework that integrates topic modeling with feature selection to improve model accuracy and interpretability. Unlike traditional methods that rely on individual words, TextNetTopics selects cohesive topics extracted via Latent Dirichlet Allocation as features for document representation, effectively reducing dimensionality while preserving the semantic structure of the text. This study evaluates the performance of TextNetTopics utilizing multiple machine learning algorithms in the M (Modeling) component, including Random Forest, Support Vector Machine, Gradient Boosting, eXtreme Gradient Boosting, and Logistic Regression. To further enhance classification performance, we introduce TextNetTopics+, an ensemblebased extension that leverages both hard voting and soft voting mechanisms to combine the strengths of multiple classifiers. Comprehensive experiments on the LitCovid and WOS datasets demonstrate that ensemble learning in TextNetTopics + significantly outperforms individual classifiers in TextNetTopics, confirming its effectiveness in improving model robustness and generalization.
  • Conference Object
    Citation - Scopus: 1
    Semant - Feature Group Selection Utilizing Fasttext-Based Semantic Word Grouping, Scoring, and Modeling Approach for Text Classification
    (Springer International Publishing AG, 2024) Voskergian, Daniel; Bakir-Gungor, Burcu; Yousef, Malik
    Text classification presents a challenge due to its high-dimensional feature space. As such, devising an effective feature selection scheme is essential. In this study, we present SEMANT, a novel hybrid filter-wrapper feature selection method that utilizes filter-based Chi-Square and the wrapper-based G-S-M approach. SEMANT incorporates fastText neural word embedding similarities to promote greater semantic inclusion in the selection of features for text classification tasks. The performance of the proposed method was investigated on the WOS-5736 and LitCovid datasets and compared with TextNetTopics, a topic modeling-based topic selection algorithm for text classification. Experimental results confirm that the proposed approach outperforms its alternative.
  • Conference Object
    Citation - WoS: 8
    Citation - Scopus: 12
    SVM-RCE-R Optimization of Scoring Function for SVM-RCE
    (Springer International Publishing AG, 2021) Yousef, Malik; Jabeer, Amhar; Bakir-Gungor, Burcu
    Gene expression data classification provides a challenge in classification due to it having high dimensionality and a relatively small sample size. Different feature selection approaches have been used to overcome this issue and SVM-RCE being one of the more successful approach. This study is a continuation of two previous research studies SVM-RCE and SVM-RCE-R. SVM-RCE-R suggests a new approach in the scoring function for the clusters, showing that for some different combination of weights the performance was improved. The aim of this study is to find the optimal weights for the scoring function suggested in the study of SVM-RCE-R using optimization approaches. We have discovered that finding the optimal weights for the scoring function would improve the performance of the SVM-RCE-in most cases. We have shown that in some cases the performance is increased dramatically by 10% in terms of accuracy and AUC. By increasing the performance of the algorithm, it is more likely that we can extract subset genes relating to the class association of a microarray sample.
  • Conference Object
    Leveraging MicroRNA-Gene Associations With Mirgedinet: An Intelligent Approach for Enhanced Classification of Breast Cancer Molecular Subtypes
    (Springer International Publishing AG, 2025) Qumsiyeh, Emma; Bakir-Gungor, Burcu; Yousef, Malik
    Understanding the molecular subtypes of breast cancer is crucial for advancing targeted therapies and precision medicine. For the BRCA molecular subtype prediction problem, this study employs miRGediNET, a machinelearning approach that integrates data from miRTarBase, DisGeNET, and HMDD databases to investigate shared gene associations between microRNA (miRNA) activity and disease mechanisms. Using the BRCA LumAB_Her2Basal dataset, we evaluate miRGediNET's performance against traditional feature selection methods, including CMIM, mRmR, Information Gain (IG), SelectKBest (SKB), Fast Correlation-Based Filter (FCBF), and XGBoost (XGB). These feature selection techniques were assessed using various classification algorithms including Random Forest (RF), Support Vector Machine (SVM), LogitBoost, Decision Tree, and AdaBoost, all executed with default parameters. The feature selection methods were tested using Monte Carlo Cross-Validation, where performance metrics obtained for each iteration were averaged to ensure robustness. Our findings reveal that miRGediNET outperforms traditional methods in accuracy and Area Under the Curve (AUC), emphasizing its superior capability to identify key genes that bridge miRNA interactions and breast cancer mechanisms. Notably, both miRGediNET and Information Gain (IG) feature selection consistently identified ESR1, a critical biomarker frequently reported in recent research associated with breast cancer prognosis and resistance to endocrine therapies. This integrative approach provides deeper biological insights into miRNA-disease interactions, paving the way for enhanced patient stratification, biomarker discovery, and personalized medicine strategies. The miRGediNET tool, developed on the KNIME platform, offers a practical resource for further exploration in the field of bioinformatics and oncology.
  • Conference Object
    Citation - Scopus: 4
    Deep Learning Based Employee Attrition Prediction
    (Springer International Publishing AG, 2023) Gurler, Kerem; Pak, Burcu Kuleli; Gungor, Vehbi Cagri
    Employee attrition is a critical issue for the business sectors as leaving employees cause various types of difficulties for the company. Some studies exist on examining the reasons for this phenomenon and predicting it with Machine Learning algorithms. In this paper, the causes for employee attrition is explored in three datasets, one of them being our own novel dataset and others obtained from Kaggle. Employee attrition was predicted with multiple Machine Learning and Deep Learning algorithms with feature selection and hyperparameter optimization and their performances are evaluated with multiple metrics. Deep Learning methods showed superior performances in all of the datasets we explored. SMOTE Tomek Links were utilized to oversample minority classes and effectively tackle the problem of class imbalance. Best performing methods were Deep Random Forest on HR Dataset from Kaggle and Neural Network for IBM and Adesso datasets with F1 scores of 0.972, 0.642 and 0.853, respectively.
  • Conference Object
    Colorectal Cancer Prediction via Applying Recursive Cluster Elimination With Intra-Cluster Feature Elimination on Metagenomic Pathway Data
    (Springer International Publishing AG, 2024) Temiz, Mustafa; Kuzudisli, Cihan; Yousef, Malik; Bakir-Gungor, Burcu
    Advances in next-generation sequencing and in "-omics" technologies enable the characterization of the human gut microbiome. Colorectal cancer (CRC), the third most common cancer worldwide, is caused by genetic mutations, environmental influences, and abnormalities in the gut microbiota. The aim of this study is to identify pathways that influence host metabolism in CRC patients. The CRC-related metagenomic dataset used in this study contains the relative abundance values of 551 pathways calculated for 1262 samples. Here, two different approaches based on the feature grouping reduce the number of features by considering relevant features as groups, eliminate irrelevant features, and perform classification. The recursive cluster elimination with intra-cluster feature elimination (RCE-IFE) approach achieves anAUCof 0.72 using an average of 66.2 features on CRC-associated metagenomics dataset. In these experiments, P163-PWY: L-lysine fermentation to acetate and butanoate and PWY-6151: S-adenosyl-L-methionine cycle I pathways are identified as potential biomarkers associated with CRC. These experiments also reduce the number of features reported by both approaches in P163-PWY: L-lysine fermentation to acetate and butanoate and PWY-6151: Sadenosyl-L-methionine cycle I pathways reported by both approaches are considered possible CRC-related biomarkers. This study contributes to the molecular diagnosis and treatment of colorectal cancer by revealing the pathways associated with CRC. Our results are promising for the study of the gut microbiota and its role in CRC.