Mirceva, Georgina
Preferred name
Mirceva, Georgina
Official Name
Mirceva, Georgina
Main Affiliation
68 results
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Item type:Publication, Automating Feature Extraction from Entity-Relation Models: Experimental Evaluation of Machine Learning Methods for Relational Learning(MDPI AG, 2024-04-01); ; <jats:p>With the exponential growth of data, extracting actionable insights becomes resource-intensive. In many organizations, normalized relational databases store a significant portion of this data, where tables are interconnected through some relations. This paper explores relational learning, which involves joining and merging database tables, often normalized in the third normal form. The subsequent processing includes extracting features and utilizing them in machine learning (ML) models. In this paper, we experiment with the propositionalization algorithm (i.e., Wordification) for feature engineering. Next, we compare the algorithms PropDRM and PropStar, which are designed explicitly for multi-relational data mining, to traditional machine learning algorithms. Based on the performed experiments, we concluded that Gradient Boost, compared to PropDRM, achieves similar performance (F1 score, accuracy, and AUC) on multiple datasets. PropStar consistently underperformed on some datasets while being comparable to the other algorithms on others. In summary, the propositionalization algorithm for feature extraction makes it feasible to apply traditional ML algorithms for relational learning directly. In contrast, approaches tailored specifically for relational learning still face challenges in scalability, interpretability, and efficiency. These findings have a practical impact that can help speed up the adoption of machine learning in business contexts where data is stored in relational format without requiring domain-specific feature extraction.</jats:p> - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Fake News Detection by Using Doc2Vec Representation Model and Various Classification Algorithms(IEEE, 2021); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Ski Slopes Digitalization and Rating Analysis of Ski Resorts in Mavrovo and Popova Shapka(IEEE, 2019); ; Winter months are about snow, and there is no greater benefit for people that live in polluted cities to spend some time on high mountains in fresh air and sun. Therefore, the economical developing of location that offers such winter recreational activities is very important. In this direction, this paper aims to use Geographic Information Systems (GIS) to improve the understanding of the relationship between the ski resorts and a user rating of satisfactory opinion through data analysis and digitalization. GIS is a powerful digitalization and analytical tool and most importantly, the users can access the platform from anywhere and anytime. As a case study, we have considered two major ski resorts in our country, as well as rating data for customer satisfaction scores. Furthermore, as a showcase, we have digitalized one ski resorts slope. The results of this research will provide a framework for future analysis and the development of ski tourism. In the future, we plan to advance this further improve the data analysis with other data and advance the decision-making process. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Evaluation of diatoms biodiversity models by applying different discretization on the class attribute(IEEE, 2020-09-28); ; One of the main goals of knowledge discovery from environmental data is through data analysis to find the relationship between the living organisms, represented with the diversity of the diatoms community members, and the characteristics of the environment. This is very important information for both ecologists and decision makers. Therefore, in this paper we apply various machine learning algorithms for revealing this relationship by using different number of discretization levels for the target attribute. The target attribute represents the biodiversity index of the community and it is calculated based on the abundances of the diatoms. For building models, different types of machine learning algorithms are considered including decision trees, rule induction algorithms, neural networks and Naïve Bayes. The obtained models are also examined regarding resistance to over-fitting, as well as statistical significance. - Some of the metrics are blocked by yourconsent settings
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Item type:Publication, Content Engineering for State-of-the-art SEO Digital Strategies by Using NLP and ML(IEEE, 2021-06-11) - Some of the metrics are blocked by yourconsent settings
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