PRZEGLĄD METOD SELEKCJI CECH UŻYWANYCH W DIAGNOSTYCE CZERNIAKA

Magdalena Michalska

doi:10.35784/iapgos.2455

Open full text

PDF

Published: Mar 31, 2021

DOI: https://doi.org/10.35784/iapgos.2455

DOI

https://doi.org/10.35784/iapgos.2455

Authors

Magdalena Michalska

mmagamichalska@gmail.com

Lublin Univeristy of Technology, Department of Electronics and Computer Science

http://orcid.org/0000-0002-0874-3285

Abstract

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.

Keywords:

feature selection methods, filter methods, wrappers methods, embedded methods

References

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

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

Michalska, M. (2021). OVERVIEW OF FEATURE SELECTION METHODS USED IN MALIGNANT MELANOMA DIAGNOSTICS. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 11(1), 32–35. https://doi.org/10.35784/iapgos.2455

Article Sidebar

Main Article Content

DOI

Authors

Abstract

Keywords:

References

Article Details

License