PRZEGLĄD METOD SELEKCJI CECH UŻYWANYCH W DIAGNOSTYCE CZERNIAKA

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].
DOI: https://doi.org/10.1109/AEECT.2017.8257738   Google Scholar

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.
DOI: https://doi.org/10.1109/TEVC.2016.2577548   Google Scholar

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.
DOI: https://doi.org/10.1111/j.1467-9876.2011.01032.x   Google Scholar

Bolón-Canedo V., Remeseiro B.: Feature selection in image analysis: a survey. Artif Intell Rev 53/2020, 2905–2931.
DOI: https://doi.org/10.1007/s10462-019-09750-3   Google Scholar

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.
DOI: https://doi.org/10.1111/j.1600-0846.2007.00251.x   Google Scholar

Chmielnicki W.: Efektywne metody selekcji cech i rozwiązywania problemu wieloklasowego w nadzorowanej klasyfikacji danych. Rozprawa doktorska. Instytut Podstawowych Problemów Techniki PAN, Kraków 2012.
  Google Scholar

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].
DOI: https://doi.org/10.1016/S0004-3702(03)00079-1   Google Scholar

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.
DOI: https://doi.org/10.1109/EMBC.2012.6346458   Google Scholar

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.
DOI: https://doi.org/10.1109/10.312091   Google Scholar

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.
  Google Scholar

Guyon I., Elisseeff A.: An introduction to variable and feature selection. Journal of Machine Learning Research 3/2003, 1157–1182.
  Google Scholar

Hall M., Smith Lloyd A.: Practical feature subset selection for machine learning. Springer 1998.
  Google Scholar

Hall M.: Correlation-based feature selection for machine learning. Department of Computer Science 19/2000.
  Google Scholar

https://moredvikas.wordpress.com/2018/10/09/machine-learning-introduction-to-feature-selection-variable-selection-or-attribute-selection-or-dimensionality-reduction/
  Google Scholar

Huang J., Ling C. X.: Using AUC and accuracy in evaluating learning algorithms. IEEE Trans Knowledge Data Eng. 17(3)/2005, 299–310.
DOI: https://doi.org/10.1109/TKDE.2005.50   Google Scholar

Keerthi Vasan K., Surendiran B.,: Dimensionality reduction using Principal Component Analysis for network intrusion detection. Perspectives in Science 8/2016, 510–512.
DOI: https://doi.org/10.1016/j.pisc.2016.05.010   Google Scholar

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.
DOI: https://doi.org/10.1186/s12885-018-4465-8   Google Scholar

Kira K., Rendell L. A.: A practical approach to feature selection. Machine Learning Proceedings 1992, 249–256.
DOI: https://doi.org/10.1016/B978-1-55860-247-2.50037-1   Google Scholar

Kononenko I.: Estimating attributes: Analysis and extensions of Relief. L. De Raedt, & F. Bergadano (Eds.): Machine Learning: ECML-94 1994, 171–182.
DOI: https://doi.org/10.1007/3-540-57868-4_57   Google Scholar

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.
DOI: https://doi.org/10.1109/JSTARS.2013.2262926   Google Scholar

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.
DOI: https://doi.org/10.1109/TKDE.2005.66   Google Scholar

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.
DOI: https://doi.org/10.1109/TEVC.2011.2166158   Google Scholar

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.
  Google Scholar

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.
DOI: https://doi.org/10.1016/j.cmpb.2017.07.009   Google Scholar

Pal M., Foody G. M.,: Feature selection for classification of hyperspectral data by SVM. IEEE Trans Geosci Remote Sens 48(5)/2010, 2297–2307.
DOI: https://doi.org/10.1109/TGRS.2009.2039484   Google Scholar

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.
DOI: https://doi.org/10.1016/j.neucom.2016.05.103   Google Scholar

Ramezani M, Karimian A, Moallem P.: Automatic Detection of Malignant Melanoma using Macroscopic Images. J Med Signals Sens. 4(4)/2014, 281–290.
DOI: https://doi.org/10.4103/2228-7477.144052   Google Scholar

Robnik-Šikonja M., Kononenko I.: Theoretical and empirical analysis of ReliefF and RReliefF. Machine learning 53(1–2)/2003, 23–69.
DOI: https://doi.org/10.1023/A:1025667309714   Google Scholar

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.
DOI: https://doi.org/10.1049/iet-ipr.2016.0681   Google Scholar

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.
  Google Scholar

Stapor K., Automatyczna klasyfikacja obiektów. Akademicka Oficyna Wydawnicza EXIT, Warszawa 2005.
  Google Scholar

UCI Machine Learning Repository [http://archive.ics.uci.edu/ml/datasets.html].
  Google Scholar

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.
DOI: https://doi.org/10.1007/978-3-319-97304-3_56   Google Scholar

Witten I. H., Frank E., Hall M. A.: Data mining: Practical machine learning tools and techniques. Morgan Kaufmann 2011.
  Google Scholar

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.
DOI: https://doi.org/10.1109/TMI.2016.2633551   Google Scholar

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.
DOI: https://doi.org/10.1109/TEVC.2015.2504420   Google Scholar

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.
DOI: https://doi.org/10.1593/neo.07121   Google Scholar

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.
DOI: https://doi.org/10.2498/cit.2005.01.06   Google Scholar

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].
DOI: https://doi.org/10.4236/jcc.2015.33013   Google Scholar