A novel feature selection algorithm using multi-objective improved honey badger algorithm and strength pareto evolutionary algorithm-II
المؤلفون المشاركون
Papasani, Anusha
Devarakonda, Nagaraju
المصدر
Journal of Engineering Research
العدد
المجلد 11، العدد 2 B (30 يونيو/حزيران 2023)، ص ص. 71-93، 23ص.
الناشر
جامعة الكويت مجلس النشر العلمي
تاريخ النشر
2023-06-30
دولة النشر
الكويت
عدد الصفحات
23
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
Feature selection is an important task in classification that removes redundant or irrelevant features from the dataset.
Many researchers favor a multi-objective feature selection approach.
However, these approaches fail to maintain high classification accuracy while removing redundancy in the features.
In this work, a wrapper-based feature selection technique is proposed using a hybrid of the Multi-Objective Honey Badger Algorithm (MOHBA) and the Strength Pareto Evolutionary Algorithm-II, called MOHBSP2, to balance classification accuracy and redundancy removal.
Classification accuracy improvement and the removal of redundant features are considered the multi-objective optimization functions of the proposed multi-objective feature selection technique.
The Levy flight algorithm is used to initialize the population and enhance the exploration and exploitation of MOHBA.
The regularized Extreme Learning Machine (ELM) is used to classify the selected features.
To evaluate the performance of the proposed feature selection technique, 18 benchmark datasets are used and results are compared with the four well-known multi-objective feature selection techniques in terms of accuracy, hamming loss, ranking loss, mean value, standard deviation, feature length, and training time.
The proposed approach achieved a maximum accuracy of 99% with the maximum value of selected features as 80.
The minimum value of hamming loss, ranking loss, mean value, and standard deviation value achieved by the proposed approach are 0.0092, 0.0003, 0.018, and 0.001, respectively.
The experimental results show that the proposed approach can improve classification accuracy and remove redundancy in large datasets.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Papasani, Anusha& Devarakonda, Nagaraju. 2023. A novel feature selection algorithm using multi-objective improved honey badger algorithm and strength pareto evolutionary algorithm-II. Journal of Engineering Research،Vol. 11, no. 2 B, pp.71-93.
https://search.emarefa.net/detail/BIM-1604340
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Papasani, Anusha& Devarakonda, Nagaraju. A novel feature selection algorithm using multi-objective improved honey badger algorithm and strength pareto evolutionary algorithm-II. Journal of Engineering Research Vol. 11, no. 2 B (Jun. 2023), pp.71-93.
https://search.emarefa.net/detail/BIM-1604340
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Papasani, Anusha& Devarakonda, Nagaraju. A novel feature selection algorithm using multi-objective improved honey badger algorithm and strength pareto evolutionary algorithm-II. Journal of Engineering Research. 2023. Vol. 11, no. 2 B, pp.71-93.
https://search.emarefa.net/detail/BIM-1604340
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references : p. 81-83
رقم السجل
BIM-1604340
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