Assesing the stability and selection performance of feature selection methods under different data complexity
المؤلفون المشاركون
al-Husni, Umaymah
Starkey, Andrew
المصدر
The International Arab Journal of Information Technology
العدد
المجلد 19، العدد 3A (s) (31 مايو/أيار 2022)، ص ص. 442-455، 14ص.
الناشر
جامعة الزرقاء عمادة البحث العلمي
تاريخ النشر
2022-05-31
دولة النشر
الأردن
عدد الصفحات
14
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
Our study aims to investigate the stability and the selection accuracy of feature selection performance under different data complexity.
The motivation behind this investigation is that there are significant contributions in the research community from examining the effect of complex data characteristics such as overlapping classes or non-linearity of the decision boundaries on the classification algorithm's performance; however, relatively few studies have investigated the stability and the selection accuracy of feature selection methods with such data characteristics.
Also, this study is interested in investigating the interactive effects of the classes overlapped with other data challenges such as small sample size, high dimensionality associated with irrelevant features, and imbalance classes to provide meaningful insights into the root causes for feature selection methods misdiagnosing the relevant features among different real-world data challenges.
This analysis will be extended to real-world data to guide the practitioners and researchers in choosing the correct feature selection methods that are more appropriate for a particular dataset.
Our study outcomes indicate that using feature selection techniques with datasets of different characteristics may generate different subsets of features under variations to the training data showing that small sample size and overlapping classes have the highest impact on the stability and selection accuracy of feature selection performance, among other data challenges that have been investigated in this study.
Also, in this study, we will provide a survey on the current state of research in the feature selection stability context to highlight the area that requires more attention for other researchers.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
al-Husni, Umaymah& Starkey, Andrew. 2022. Assesing the stability and selection performance of feature selection methods under different data complexity. The International Arab Journal of Information Technology،Vol. 19, no. 3A (s), pp.442-455.
https://search.emarefa.net/detail/BIM-1437109
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
al-Husni, Umaymah& Starkey, Andrew. Assesing the stability and selection performance of feature selection methods under different data complexity. The International Arab Journal of Information Technology Vol. 19, no. 3A (Special issue) (2022), pp.442-455.
https://search.emarefa.net/detail/BIM-1437109
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
al-Husni, Umaymah& Starkey, Andrew. Assesing the stability and selection performance of feature selection methods under different data complexity. The International Arab Journal of Information Technology. 2022. Vol. 19, no. 3A (s), pp.442-455.
https://search.emarefa.net/detail/BIM-1437109
نوع البيانات
مقالات
لغة النص
الإنجليزية
رقم السجل
BIM-1437109
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