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Przegląd metod selekcji cech używanych w diagnostyce czerniaka
Języki publikacji
Abstrakty
Currently, a large number of trait selection methods are used. They are becoming more and more of interest among researchers. Some of the methods are of course used more frequently. The article describes the basics of selection-based algorithms. FS methods fall into three categories: filter wrappers, embedded methods. Particular attention was paid to finding examples of applications of the described methods in the diagnosis of skin melanoma.
Obecnie stosuje się wiele metod selekcji cech. Cieszą się coraz większym zainteresowaniem badaczy. Oczywiście niektóre metody są stosowane częściej. W artykule zostały opisane podstawy działania algorytmów opartych na selekcji. Metody selekcji cech należące dzielą się na trzy kategorie: metody filtrowe, metody opakowujące, metody wbudowane. Zwrócono szczególnie uwagę na znalezienie przykładów zastosowań opisanych metod w diagnostyce czerniaka skóry.
Rocznik
Tom
Strony
32--35
Opis fizyczny
Bibliogr. 39 poz., rys., tab., wykr.
Twórcy
autor
- Lublin University of Technology, Department of Electronics and Information Technology, Lublin, Poland
Bibliografia
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- [3] Andersen S. W., Runger G. C.: Automated feature extraction from profiles with application to a batch fermentation process. Journal of the Royal Statistical Society: Series C (Applied Statistics) 61(2)/2012, 327–344.
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- [5] Celebi M. E., Aslandogan Y. A., Stoecker W. V., Iyatomi H., Oka H., Chen X.: Unsupervised border detection in dermoscopy images. Skin Res Technol. 13/2007, 1–9.
- [6] Chmielnicki W.: Efektywne metody selekcji cech i rozwiązywania problemu wieloklasowego w nadzorowanej klasyfikacji danych. Rozprawa doktorska. Instytut Podstawowych Problemów Techniki PAN, Kraków 2012.
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- [17] Khan M. A., Tallha A., Muhammad S., Aamir S., Khursheed A., Musaed A., Syed I. H., Abdualziz A.: An implementation of normal distribution based segmentation and entropy-controlled features selection for skin lesion detection and classification. BMC Cancer 18(1)/2018, 638.
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- [20] Kuo B. C., Ho H. H., Li C. H., Hung C. C., Taur J. S.: A kernel-based feature selection method for SVM with RBF kernel for hyperspectral image classification. IEEE J Sel Top Appl Earth Obs Remote Sens 7(1)/2014, 317–326.
- [21] Liu H., Yu L.: Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on knowledge and data engineering 17(4)/2005, 491–502.
- [22] Neshatian K., Zhang M., Andreae P.: A filter approach to multiple feature construction for symbolic learning classifiers using genetic programming. IEEE Trans. Evol. Comput. 16(5)/2012, 645–661.
- [23] Oliveira R. B., Pereira A. S., Tavaresa J. M. R. S.: Skin lesion computational diagnosis of dermoscopic images: Ensemble models based on input feature manipulation. JMRS Tavares – Computer methods and programs Computer Methods and Programs in Biomedicine 149/2017, 43–53.
- [24] Oliveira R. B., Pereira A. S., Tavaresa J. M. R. S.: Skin lesion computational diagnosis of dermoscopic images: Ensemble models based on input feature manipulation. JMRS Tavares – Computer methods and programs Computer Methods and Programs in Biomedicine 149/2017, 43–53.
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- [26] Qi C., Zhou Z., Sun Y., Song H., Hu L., Wang Q.: Feature selection and multiple kernel boosting framework based on PSO with mutation mechanism for hyperspectral classification. Neurocomputing 220/2017, 181–190.
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- [29] Sadri A. R., Azarianpour S., Zekri M., Celebi M. E., Sadri S.: WN-based approach to melanoma diagnosis from dermoscopy images. IET Image Process. 11(7)/2017, 475–482.
- [30] Shahid M., Khan S.: Dermoscopy Images classification based on color, texture and shape features using SVM. The 3rd International Conference on Next Generation Computing (INC GC2017b) 2017, 243–245.
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- [33] Ul Ain B., Xue B., Al-Sahaf H., Zhang M.: Genetic programming for feature selection and feature construction in skin cancer image classification. Pacific Rim International Conference on Artificial Intelligence, Springer 2018, 732–745.
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- [35] Xie F., Fan H., Li Y., Jiang Z., Meng R., Bovik A.: Melanoma classification on dermoscopy images using a neural network ensemble model, IEEE Transactions on Medical Imaging 36(3)/2017, 849–858.
- [36] Xue B., Zhang M., Browne W. N., Yao X.: A survey on evolutionary computation approaches to feature selection. IEEE Trans. Evol. Comput. 20(4)/2016, 606–626.
- [37] Yu J., Almal A. A., Dhanasekaran S. M.,Ghosh D., Worzel W. P., Chinnaiyan A., M.: Feature selection and molecular classification of cancer using genetic programming. Neoplasia 9(4)/2007, 292–303.
- [38] Zagrouba E., Barhoumi W.: An accelerated system for melanoma diagnosis based on subset feature selection. Journal of Computing and Information Technology – CIT 13(1)/2005, 69–82.
- [39] Zhou X., Wang J. J.: Feature selection for image classification based on a new ranking criterion. Journal of Computer and Communications 3/2015, 74–79 [http://doi.org/10.4236/jcc.2015.33013].
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ed027ae7-ff68-4696-ab94-802a53c3470f