American Psychological Association 6th edition

Deepa, B. G., & Senthil, S. (o. J.). Chapter 11 Constructive Effect of Ranking Optimal Features Using Random Forest, Support Vector Machine and Naïve Bayes for Breast Cancer Diagnosis [Electronic]. Big Data Analytics and Intelligence: A Perspective for Health Care, ((2020), Seite 189-202), , Seite 189-202. https://doi.org/10.1108/978-1-83909-099-820201014

ISO-690 (author-date, English)

DEEPA, B. G. und SENTHIL, S., [no date]. Chapter 11 Constructive Effect of Ranking Optimal Features Using Random Forest, Support Vector Machine and Naïve Bayes for Breast Cancer Diagnosis. Big Data Analytics and Intelligence: A Perspective for Health Care. No. (2020), Seite 189-202, p. , Seite 189-202. DOI 10.1108/978-1-83909-099-820201014.

Modern Language Association 9th edition

Deepa, B. G., und S. Senthil. „Chapter 11 Constructive Effect of Ranking Optimal Features Using Random Forest, Support Vector Machine and Naïve Bayes for Breast Cancer Diagnosis“. Big Data Analytics and Intelligence: A Perspective for Health Care, electronic, Nr. (2020), Seite 189-202, Emerald Group Publishing Limited [Erscheinungsort nicht ermittelbar] : Emerald Publishing Limited, S. , Seite 189-202, https://doi.org/10.1108/978-1-83909-099-820201014.

Mohr Siebeck - Recht (Deutsch - Österreich)

Deepa, B. G./Senthil, S.: Chapter 11 Constructive Effect of Ranking Optimal Features Using Random Forest, Support Vector Machine and Naïve Bayes for Breast Cancer Diagnosis, Big Data Analytics and Intelligence: A Perspective for Health Care , Seite 189-202.

Emerald - Harvard

Deepa, B.G. und Senthil, S. (o. J.). „Chapter 11 Constructive Effect of Ranking Optimal Features Using Random Forest, Support Vector Machine and Naïve Bayes for Breast Cancer Diagnosis“, Big Data Analytics and Intelligence: A Perspective for Health Care, Emerald Group Publishing Limited [Erscheinungsort nicht ermittelbar] : Emerald Publishing Limited, Bingley, UK, No. (2020), Seite 189-202, S. , Seite 189-202.

Achtung: Diese Zitate sind unter Umständen nicht zu 100% korrekt.