Tytuł artykułu
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Warianty tytułu
Konferencja
Federated Conference on Computer Science and Information Systems (17 ; 04-07.09.2022 ; Sofia, Bulgaria)
Języki publikacji
Abstrakty
In this work, we propose a new parameter to study the effectiveness of classifiers - the AUC (area under curve) of the balanced accuracy curve (BAC) on data with different balance degrees - we compare its effectiveness with the popular AUC parameters for the ROC and PR curve. We use a global kNN classifier with typical metrics to verify the utility of the new parameter. BAC, ROC and PR curves generate similar results, the advantage of BAC is its simplicity of implementation and ease of interpretation of results.
Rocznik
Tom
Strony
149--156
Opis fizyczny
Bibliogr. 15 poz., wz., wykr.
Twórcy
autor
- Scientific Circle of Robotics UWM in Olsztyn ul. Słoneczna 54, 10-710 Olsztyn, Poland
autor
- Scientific Circle of Robotics UWM in Olsztyn ul. Słoneczna 54, 10-710 Olsztyn, Poland
autor
- University of Warmia and Mazury, in Olsztyn ul. Słoneczna 54, 10-710 Olsztyn, Poland
Bibliografia
- 1. Woodward, P. M. (1953). Probability and information theory with applications to radar. London: Pergamon Press.
- 2. Peterson, W., Birdsall, T., Fox, W. (1954). The theory of signal detectability, Transactions of the IRE Professional Group on Information Theory, 4, 4, pp. 171 - 212.
- 3. Manning, C., Schutze, H. (1999). Foundations of statistical natural language processing. MIT Press
- 4. Raghavan, V., Bollmann, P., Jung, G. S. (1989). A critical investigation of recall and precision as measures of retrieval system performance. ACM Trans. Inf. Syst., 7, 205–229.
- 5. Davis, J., Goadrich, M.: 2006. The relationship between Precision-Recall and ROC curves. In Proceedings of the 23rd international conference on Machine learning (ICML ’06). Association for Computing Machinery, New York, NY, USA, 233–240. https://doi.org/10.1145/1143844.1143874
- 6. Saito T., and Rehmsmeier M. 2015. "The Precision-Recall Plot Is More Informative Than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets." PLoS ONE. 10(3): e0118432
- 7. Williams, C.K.I. 2021. "The Effect of Class Imbalance on Precision-Recall Curves." Neural Computation 33(4): 853–857.
- 8. Morzy, Tadeusz. Eksploracja danych. Red. . Warszawa: Wydawnictwo Naukowe PWN, 2013, 566 s. ISBN 978-83-01-17175-9
- 9. Hastie T., Friedman J., Tibshirani R. (2001) The Elements of Statistical Learning. Springer Series in Statistics. Springer, New York, NY.
- 10. Qimin Cao, Lei La, Hongxia Liu, and Si Han. Mixed Weighted KNN for Imbalanced Datasets [J]. Int J Performability Eng, 2018, 14(7): 1391-1400.
- 11. L., Polkowski, P., Artiemjew, “Granular Computing in Decision Approximation - An Application of Rough Mereology,” in: Intelligent Systems Reference Library 77, Springer, ISBN 978-3-319-12879-5, 2015, pp. 1-422.
- 12. Japkowicz, N., Shah, M. (2011). Evaluating Learning Algorithms: A Classification Perspective. Cambridge: Cambridge University Press. http://dx.doi.org/10.1017/CBO9780511921803
- 13. Metrics definition: manhattan, euclidean, canberra, cosine https://www.itl.nist.gov/div898/software/dataplot/homepage.htm
- 14. epsilonHamming Metric definition: In: Polkowski, L., Artiemjew, P.: Granular Computing in Decision Approximation - An Application of Rough Mereology, In: Intelligent Systems Reference Library 77, Springer, ISBN 978-3-319-12879-5, pp. 1–422 (2015).
- 15. UCI Machine Learning Repository, https://archive.ics.uci.edu/ml/index.php. Last accessed 12 Apr 2022
Uwagi
1. This work has been supported by the grant from Ministry of Science and Higher Education of the Republic of Poland under the project number 23.610.007-000.
2. Track 1: 17th International Symposium on Advanced Artificial Intelligence in Applications
3. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c9fe14e9-1ffb-4cf1-8b8f-02b782917e64