@article{MTMT:33702022,
	title = {A Comparative Study of the Use of Stratified Cross-Validation and Distribution-Balanced Stratified Cross-Validation in Imbalanced Learning},
	url = {https://m2.mtmt.hu/api/publication/33702022},
	author = {Szeghalmy, Szilvia and Fazekas, Attila},
	doi = {10.3390/s23042333},
	journal-iso = {SENSORS-BASEL},
	journal = {SENSORS},
	volume = {23},
	unique-id = {33702022},
	abstract = {Nowadays, the solution to many practical problems relies on machine learning tools. However, compiling the appropriate training data set for real-world classification problems is challenging because collecting the right amount of data for each class is often difficult or even impossible. In such cases, we can easily face the problem of imbalanced learning. There are many methods in the literature for solving the imbalanced learning problem, so it has become a serious question how to compare the performance of the imbalanced learning methods. Inadequate validation techniques can provide misleading results (e.g., due to data shift), which leads to the development of methods designed for imbalanced data sets, such as stratified cross-validation (SCV) and distribution optimally balanced SCV (DOB-SCV). Previous studies have shown that higher classification performance scores (AUC) can be achieved on imbalanced data sets using DOB-SCV instead of SCV. We investigated the effect of the oversamplers on this difference. The study was conducted on 420 data sets, involving several sampling methods and the DTree, kNN, SVM, and MLP classifiers. We point out that DOB-SCV often provides a little higher F1 and AUC values for classification combined with sampling. However, the results also prove that the selection of the sampler–classifier pair is more important for the classification performance than the choice between the DOB-SCV and the SCV techniques.},
	year = {2023},
	eissn = {1424-8220},
	orcid-numbers = {Fazekas, Attila/0000-0001-6893-3067}
}

@article{MTMT:32807025,
	title = {A Highly Adaptive Oversampling Approach to Address the Issue of Data Imbalance},
	url = {https://m2.mtmt.hu/api/publication/32807025},
	author = {Szeghalmy, Szilvia and Fazekas, Attila},
	doi = {10.3390/computers11050073},
	journal-iso = {COMPUTERS},
	journal = {COMPUTERS},
	volume = {11},
	unique-id = {32807025},
	keywords = {Oversampling; imbalanced learning},
	year = {2022},
	eissn = {2073-431X},
	orcid-numbers = {Fazekas, Attila/0000-0001-6893-3067}
}

@inproceedings{MTMT:32674673,
	title = {Városi terepfelületek osztályozásának javítása mintavételezési módszerekkel},
	url = {https://m2.mtmt.hu/api/publication/32674673},
	author = {Szeghalmy, Szilvia and Fazekas, Attila},
	booktitle = {Az elmélet és a gyakorlat találkozása a térinformatikában XII.: Theory meets practice in GIS},
	unique-id = {32674673},
	year = {2021},
	pages = {295-300},
	orcid-numbers = {Fazekas, Attila/0000-0001-6893-3067}
}

@inproceedings{MTMT:30889799,
	title = {Smartphone based Driver-Assistance System for Urban Environments},
	url = {https://m2.mtmt.hu/api/publication/30889799},
	author = {Szeghalmy, Szilvia and Fazekas, Attila and Zichar, Marianna and Tamas, Ferenc Bence and Dávid, Uszkai},
	booktitle = {2019 International Conference on Information and Digital Technologies (IDT)},
	doi = {10.1109/DT.2019.8813342},
	unique-id = {30889799},
	year = {2019},
	pages = {437-442},
	orcid-numbers = {Fazekas, Attila/0000-0001-6893-3067; Zichar, Marianna/0000-0002-1943-6053}
}

@inproceedings{MTMT:3379363,
	title = {Detection of lanes and traffic signs painted on road using on-board camera},
	url = {https://m2.mtmt.hu/api/publication/3379363},
	author = {Bente, Tamás Ferencz and Szeghalmy, Szilvia and Fazekas, Attila},
	booktitle = {2018 IEEE International Conference on Future IoT Technologies, Future IoT 2018},
	unique-id = {3379363},
	year = {2018},
	pages = {1-7},
	orcid-numbers = {Fazekas, Attila/0000-0001-6893-3067}
}

@inproceedings{MTMT:3379356,
	title = {Helymeghatározással kiegészített sávdetektáló rendszer fejlesztése},
	url = {https://m2.mtmt.hu/api/publication/3379356},
	author = {Uszkai, Dávid and Bente, Tamás Ferencz and Fazekas, Attila and Szeghalmy, Szilvia},
	booktitle = {Az elmélet és a gyakorlat találkozása a térinformatikában IX.},
	unique-id = {3379356},
	year = {2018},
	pages = {363-369},
	orcid-numbers = {Fazekas, Attila/0000-0001-6893-3067}
}

@inproceedings{MTMT:3361466,
	title = {Detection of Lanes Using Smartphone Application},
	url = {https://m2.mtmt.hu/api/publication/3361466},
	author = {Bente, Tamás Ferencz and Szeghalmy, Szilvia and Fazekas, Attila},
	booktitle = {IX. magyar számítógépes grafika és geometria konferencia, GRAFGEO 2018},
	unique-id = {3361466},
	year = {2018},
	pages = {99-102},
	orcid-numbers = {Fazekas, Attila/0000-0001-6893-3067}
}

@mastersthesis{MTMT:3122613,
	title = {Biológiai és anyagtudományi kutatások támogatása képfeldolgozási módszerekkel},
	url = {https://m2.mtmt.hu/api/publication/3122613},
	author = {Szeghalmy, Szilvia},
	unique-id = {3122613},
	year = {2016}
}

@{MTMT:3070984,
	title = {Xbox Kinect szenzor alkalmazási lehetőségei a felszínmodellezésben},
	url = {https://m2.mtmt.hu/api/publication/3070984},
	author = {Bertalan, László and Szeghalmy, Szilvia and Barkóczi, Norbert and Szabó, Gergely},
	booktitle = {Az elmélet és a gyakorlat találkozása a térinformatikában VII. = Theory meets practice in GIS},
	unique-id = {3070984},
	year = {2016},
	pages = {542-542},
	orcid-numbers = {Bertalan, László/0000-0002-5963-2710}
}

@article{MTMT:2965753,
	title = {A sávos szitakötő [Calopteryx splendens (Harris, 1782)] konyári-kállói imágópopulációjának morfometriai elemzése},
	url = {https://m2.mtmt.hu/api/publication/2965753},
	author = {Szalay, Petra Éva and Szeghalmy, Szilvia and Kis, Olga and Szabó, László József and Miskolczi, Margit and Fazekas, Attila and Dévai, György},
	journal-iso = {STUD ODONATOL HUNG},
	journal = {STUDIA ODONATOLOGICA HUNGARICA},
	volume = {17},
	unique-id = {2965753},
	issn = {1217-453X},
	year = {2015},
	pages = {23-44},
	orcid-numbers = {Fazekas, Attila/0000-0001-6893-3067}
}