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Department: Atılım UniversitySubject: random forest

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    Article
    Citation - WoS: 4
    Citation - Scopus: 3
    A Paired Learner-Based Approach for Concept Drift Detection and Adaptation in Software Defect Prediction
    (Mdpi, 2021) Gangwar, Arvind Kumar; Kumar, Sandeep; Mishra, Alok
    The early and accurate prediction of defects helps in testing software and therefore leads to an overall higher-quality product. Due to drift in software defect data, prediction model performances may degrade over time. Very few earlier works have investigated the significance of concept drift (CD) in software-defect prediction (SDP). Their results have shown that CD is present in software defect data and tha it has a significant impact on the performance of defect prediction. Motivated from this observation, this paper presents a paired learner-based drift detection and adaptation approach in SDP that dynamically adapts the varying concepts by updating one of the learners in pair. For a given defect dataset, a subset of data modules is analyzed at a time by both learners based on their learning experience from the past. A difference in accuracies of the two is used to detect drift in the data. We perform an evaluation of the presented study using defect datasets collected from the SEACraft and PROMISE data repositories. The experimentation results show that the presented approach successfully detects the concept drift points and performs better compared to existing methods, as is evident from the comparative analysis performed using various performance parameters such as number of drift points, ROC-AUC score, accuracy, and statistical analysis using Wilcoxon signed rank test.
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    A Prediction Study About the Pandemic Era Based on Machine Learning Methods
    (Auricle Global Society of Education and Research, 2021) Eryılmaz,M.; Eryılmaz, Meltem; Yalçınkaya,F.; Kara, Erdi; Ertan,Ö.; Kara,E.; Eryılmaz, Meltem; Kara, Erdi; Mathematics; Computer Engineering; Mathematics; Computer Engineering
    Coronavirus pandemic has been going on since late 2019, millions of people died worldwide, vaccination has recently started in many countries and new strategies are sought by countries since they are still struggling to defeat the virus. So, this research is made to predict the possible ending time of the coronavirus pandemic in Turkey using data mining and statistical studies. Data mining is a computer science study that processes large amounts of data and produces new useful information. It is especially used to support decision making in companies today. So, this project could support the decision making of authorities in developing an effective strategy against the on-going pandemic. During the research we have practiced on Turkey’s coronavirus and vaccination data between 13 January 2021 and 28 May 2021. We used Rapidminer and the Random Forest method for data mining. After all the simulations we have applied and observed during our research, it was clearly seen that vaccination parameters were decreasing the new cases. Also, the stringency index did not affect the new cases. As a conclusion of our research and observations, we think that the government should vaccinate as many people as it can in order to relax restrictions for the last time. © 2021 Authors. All rights reserved.