Browsing by Author "Kilinc D."
Now showing 1 - 5 of 5
Results Per Page
Sort Options
Item DEMIR at CLEF ehealth: The effects of selective query expansion to information retrieval(CEUR-WS, 2014) Ozturkmenoglu O.; Alpkocak A.; Kilinc D.This paper presents the details of participation of DEMIR (Dokuz Eylül University Multimedia Information Retrieval) research team to the Share/CLEF eHealth 2014. This year, we participated to task 3a: monolingual user-centered health information retrieval. In this task, we focused to apply query expansion techniques selectively to some queries to improve the performance of information retrieval. Thus, we first extracted some statistical features from queries such as length of query, sum and intersect of document frequencies of each query term etc. We develop a system to predict if a query is to be expanded or not. Then, we trained our system with previous year's data. Then, we applied a query expansion method only to the queries, which are selected by the system. The results show that the approach we proposed slightly improves our baseline retrieval performance in terms of P@10.Item Regression Analysis Based Software Effort Estimation Method(World Scientific Publishing Co. Pte Ltd, 2016) Yücalar F.; Kilinc D.; Borandag E.; Ozcift A.Estimating the development effort of a software project in the early stages of the software life cycle is a significant task. Accurate estimates help project managers to overcome the problems regarding budget and time overruns. This paper proposes a new multiple linear regression analysis based effort estimation method, which has brought a different perspective to the software effort estimation methods and increased the success of software effort estimation processes. The proposed method is compared with standard Use Case Point (UCP) method, which is a well-known method in this area, and simple linear regression based effort estimation method developed by Nassif et al. In order to evaluate and compare the proposed method, the data of 10 software projects developed by four well-established software companies in Turkey were collected and datasets were created. When effort estimations obtained from datasets and actual efforts spent to complete the projects are compared with each other, it has been observed that the proposed method has higher effort estimation accuracy compared to the other methods. © 2016 World Scientific Publishing Company.Item Collaborative filtering based course recommender using OWA operators(Institute of Electrical and Electronics Engineers Inc., 2018) Bozyigit A.; Bozyigit F.; Kilinc D.; Nasiboglu E.Recommendation systems guide users to choose the most appropriate items among numerous alternatives based on predicting their interests. Recently, it is seen that recommendation systems have become to be widely used in educational domain, especially in course recommender applications. The objectives of these systems is facilitating course selection process of students and reducing their stresses. The current course recommendation studies generally consider the most recent grades of the courses taken by students and ignore the case of repeating the course under the pass-fail or grade replacement options. However, retaking a course is the primary parameter giving opinion about tendency of the students to the courses. In this study, we propose a novel collaborative filtering (CF) based course recommendation system considering the case of repeating a course and students' grades in the course for each repetition. We experiment different Ordered Weighted Averaging (OWA) operators which aggregates grades for each student's repeated courses to enhance the recommendation quality. The normalized mean absolute error (MAE) of our approach using CF and OWA is calculated as 0,063 which is encouraging for future work. © 2018 IEEE.Item Majority vote feature selection algorithm in software fault prediction(ComSIS Consortium, 2019) Borandag E.; Ozcift A.; Kilinc D.; Yucalar F.Identification and location of defects in software projects is an important task to improve software quality and to reduce software test effort estimation cost. In software fault prediction domain, it is known that 20% of the modules will in general contain about 80% of the faults. In order to minimize cost and effort, it is considerably important to identify those most error prone modules precisely and correct them in time. Machine Learning (ML) algorithms are frequently used to locate error prone modules automatically. Furthermore, the performance of the algorithms is closely related to determine the most valuable software metrics. The aim of this research is to develop a Majority Vote based Feature Selection algorithm (MVFS) to identify the most valuable software metrics. The core idea of the method is to identify the most influential software metrics with the collaboration of various feature rankers. To test the efficiency of the proposed method, we used CM1, JM1, KC1, PC1, Eclipse Equinox, Eclipse JDT datasets and J48, NB, K-NN (IBk) ML algorithms. The experiments show that the proposed method is able to find out the most significant software metrics that enhances defect prediction performance. © 2019, ComSIS Consortium. All rights reserved.Item Multiple-classifiers in software quality engineering: Combining predictors to improve software fault prediction ability(Elsevier B.V., 2020) Yucalar F.; Ozcift A.; Borandag E.; Kilinc D.Software development projects require a critical and costly testing phase to investigate efficiency of the resultant product. As the size and complexity of project increases, manual prediction of software defects becomes a time consuming and costly task. An alternative to manual defect prediction is the use of automated predictors to focus on faulty modules and let the software engineer to examine the defective part with more detail. In this aspect, improved fault predictors will always find a software quality application project to be applied on. There are many base predictors tested-designed for this purpose. However, base predictors might be combined with an ensemble strategy to further improve to increase their performance, particularly fault-detection abilities. The aim of this study is to demonstrate fault-prediction performance of ten ensemble predictors compared to baseline predictors empirically. In our experiments, we used 15 software projects from PROMISE repository and we evaluated the fault-detection performance of algorithms in terms of F-measure (FM) and Area under the Receiver Operating Characteristics (ROC) Curve (AUC). The results of experiments demonstrated that ensemble predictors might improve fault detection performance to some extent. © 2019 Karabuk University