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001-es BibID:	BIBFORM105550
035-os BibID:	(WOS)000904875400013 (Scopus)85142492599
Első szerző:	Fazekas Attila (matematikus, informatikus)
Cím:	Optimal binning for a variance based alternative of mutual information in pattern recognition / Attila Fazekas, György Kovács
Dátum:	2023
ISSN:	0925-2312
Megjegyzések:	Mutual information (MI) is a widely used similarity measure in pattern recognition. MI uses entropy as a measure of uncertainty to quantify the structural similarity of two vectors. Replacing entropy with variance as a measure of uncertainty, an analogous class of similarity measures can be derived and estimated by regression techniques. Recently, the non-linear piecewise constant regression (PWCR) has been proposed to drive similarity measures of this scheme, leading to competitive alternatives of MI. Although PWCR is based on binning, the optimal binning technique for certain problems remained an open question. In this paper, we show mathematically that the optimal binning needs to be aligned with the expected relationship between the vectors being compared. In general, approximately optimal binnings can be found by combinatorial optimization, and in certain cases the optimal binning can be determined by k-means clustering. The theoretical findings are supported by numerical experiments that show a 2-5% increase in the AUC scores in simulated pattern recognition scenarios and improved feature rankings in feature selection problems. The results suggest that the proposed binning techniques could improve the performance of PWCR-driven similarity measures in real-world applications.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Dissimilarity Template matching Matching by tone mapping Optimal binning Explained variance
Megjelenés:	Neurocomputing. - 519 (2023), p. 135-147. -
További szerzők:	Kovács György
Pályázati támogatás:	EFOP-3.6.3-VEKOP-16-2017-00002 EFOP
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM119746
035-os BibID:	(Cikkazonosító)127556
Első szerző:	Kovács György
Cím:	mlscorecheck: Testing the consistency of reported performance scores and experiments in machine learning / György Kovács, Attila Fazekas
Dátum:	2024
ISSN:	0925-2312
Megjegyzések:	Addressing the reproducibility crisis in artificial intelligence through the validation of reported experimental results is a challenging task. It necessitates either the reimplementation of techniques or a meticulous assessment of papers for deviations from the scientific method and best statistical practices. To facilitate the validation of reported results, we have developed numerical techniques capable of identifying inconsistencies between reported performance scores and various experimental setups in machine learning problems, including binary/multiclass classification and regression. These consistency tests are integrated into the open-source package mlscorecheck, which also provides specific test bundles designed to detect systematically recurring flaws in various fields, such as retina image processing and synthetic minority oversampling.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Binary classification Multiclass classification Regression Consistency testing Performance scores Open source
Megjelenés:	Neurocomputing. - 583 (2024), p. 1-4. -
További szerzők:	Fazekas Attila (1968-) (matematikus, informatikus)
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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