An Amalgamated Approach to Bilevel Feature Selection Techniques Utilizing Soft Computing Methods for Classifying Colon Cancer
المؤلفون المشاركون
Prabhakar, Sunil Kumar
Kim, Sun-Hee
Rajaguru, Harikumar
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-10-13
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
One of the deadliest diseases which affects the large intestine is colon cancer.
Older adults are typically affected by colon cancer though it can happen at any age.
It generally starts as small benign growth of cells that forms on the inside of the colon, and later, it develops into cancer.
Due to the propagation of somatic alterations that affects the gene expression, colon cancer is caused.
A standardized format for assessing the expression levels of thousands of genes is provided by the DNA microarray technology.
The tumors of various anatomical regions can be distinguished by the patterns of gene expression in microarray technology.
As the microarray data is too huge to process due to the curse of dimensionality problem, an amalgamated approach of utilizing bilevel feature selection techniques is proposed in this paper.
In the first level, the genes or the features are dimensionally reduced with the help of Multivariate Minimum Redundancy–Maximum Relevance (MRMR) technique.
Then, in the second level, six optimization techniques are utilized in this work for selecting the best genes or features before proceeding to classification process.
The optimization techniques considered in this work are Invasive Weed Optimization (IWO), Teaching Learning-Based Optimization (TLBO), League Championship Optimization (LCO), Beetle Antennae Search Optimization (BASO), Crow Search Optimization (CSO), and Fruit Fly Optimization (FFO).
Finally, it is classified with five suitable classifiers, and the best results show when IWO is utilized with MRMR, and then classified with Quadratic Discriminant Analysis (QDA), a classification accuracy of 99.16% is obtained.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Prabhakar, Sunil Kumar& Rajaguru, Harikumar& Kim, Sun-Hee. 2020. An Amalgamated Approach to Bilevel Feature Selection Techniques Utilizing Soft Computing Methods for Classifying Colon Cancer. BioMed Research International،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1137456
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Prabhakar, Sunil Kumar…[et al.]. An Amalgamated Approach to Bilevel Feature Selection Techniques Utilizing Soft Computing Methods for Classifying Colon Cancer. BioMed Research International No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1137456
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Prabhakar, Sunil Kumar& Rajaguru, Harikumar& Kim, Sun-Hee. An Amalgamated Approach to Bilevel Feature Selection Techniques Utilizing Soft Computing Methods for Classifying Colon Cancer. BioMed Research International. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1137456
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1137456
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر