PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Overview of feature selection methods used in malignant melanoma diagnostics

Treść / Zawartość
Identyfikatory
Warianty tytułu
PL
Przegląd metod selekcji cech używanych w diagnostyce czerniaka
Języki publikacji
EN
Abstrakty
EN
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.
PL
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
Strony
32--35
Opis fizyczny
Bibliogr. 39 poz., rys., tab., wykr.
Twórcy
  • Lublin University of Technology, Department of Electronics and Information Technology, Lublin, Poland
Bibliografia
  • [1] Alquran H., Qasmieh I. A., Alqudah A. M., Alhammouri S., Alawneh E., Abughazaleh A., Hasayen F.: The melanoma skin cancer detection and classification using support vector machine. IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), Aqaba, Jordan, 2017, 1–5 [http://doi.org/10.1109/AEECT.2017.8257738].
  • [2] Al-Sahaf H., Al-Sahaf A., Xue B., Johnston M., Zhang M.: Automatically Evolving Rotation-Invariant Texture Image Descriptors by Genetic Programming. IEEE Transactions on Evolutionary Computation 21(1)/2017, 83–101.
  • [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.
  • [4] Bolón-Canedo V., Remeseiro B.: Feature selection in image analysis: a survey. Artif Intell Rev 53/2020, 2905–2931.
  • [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.
  • [7] Dash M., Liu H.: Consistency-based search in feature selection. Artificial Intelligence 151(1–2)/2003, 155–176 [http://doi.org/10.1016/S0004- 3702(03)00079-1].
  • [8] Doukas C., Stagkopoulos P., Kiranoudis C. T., Maglogiannis I.: Automated skin lesion assessment using mobile technologies and cloud platforms. Engineering in Medicine and Biology Society (EMBC) – Annual International Conference of the IEEE, 2012.
  • [9] Ercal F., Chawla A., Stoecker W.V., Lee H., Moss R. H.: Neural Network diagnosis of malignant melanoma from color images. IEEE Transactions on Biomedical Engineering 41(9)/1994, 837–845.
  • [10] Gościk, J., Łukaszuk, T.: Application of the recursive feature elimination and the relaxed linear separability feature selection algorithms to gene expression data analysis. Advances in Computer Science Research 10/2013, 39–52.
  • [11] Guyon I., Elisseeff A.: An introduction to variable and feature selection. Journal of Machine Learning Research 3/2003, 1157–1182.
  • [12] Hall M., Smith Lloyd A.: Practical feature subset selection for machine learning. Springer 1998.
  • [13] Hall M.: Correlation-based feature selection for machine learning. Department of Computer Science 19/2000.
  • [14] https://moredvikas.wordpress.com/2018/10/09/machine-learning-introduction-to-feature-selection-variable-selection-or-attribute-selection-or-dimensionality-reduction/
  • [15] Huang J., Ling C. X.: Using AUC and accuracy in evaluating learning algorithms. IEEE Trans Knowledge Data Eng. 17(3)/2005, 299–310.
  • [16] Keerthi Vasan K., Surendiran B.,: Dimensionality reduction using Principal Component Analysis for network intrusion detection. Perspectives in Science 8/2016, 510–512.
  • [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.
  • [18] Kira K., Rendell L. A.: A practical approach to feature selection. Machine Learning Proceedings 1992, 249–256.
  • [19] Kononenko I.: Estimating attributes: Analysis and extensions of Relief. L. De Raedt, & F. Bergadano (Eds.): Machine Learning: ECML-94 1994, 171–182.
  • [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.
  • [25] Pal M., Foody G. M.,: Feature selection for classification of hyperspectral data by SVM. IEEE Trans Geosci Remote Sens 48(5)/2010, 2297–2307.
  • [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.
  • [27] Ramezani M, Karimian A, Moallem P.: Automatic Detection of Malignant Melanoma using Macroscopic Images. J Med Signals Sens. 4(4)/2014, 281–290.
  • [28] Robnik-Šikonja M., Kononenko I.: Theoretical and empirical analysis of ReliefF and RReliefF. Machine learning 53(1–2)/2003, 23–69.
  • [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.
  • [31] Stapor K., Automatyczna klasyfikacja obiektów. Akademicka Oficyna Wydawnicza EXIT, Warszawa 2005.
  • [32] UCI Machine Learning Repository [http://archive.ics.uci.edu/ml/datasets.html].
  • [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.
  • [34] Witten I. H., Frank E., Hall M. A.: Data mining: Practical machine learning tools and techniques. Morgan Kaufmann 2011.
  • [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
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.