Software Defect Prediction Based on Fuzzy Weighted Extreme Learning Machine with Relative Density Information
المؤلفون المشاركون
Zheng, Shang
Gai, Jinjing
Yu, Hualong
Zou, Haitao
Gao, Shang
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-18، 18ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-11-19
دولة النشر
مصر
عدد الصفحات
18
التخصصات الرئيسية
الملخص EN
To identify software modules that are more likely to be defective, machine learning has been used to construct software defect prediction (SDP) models.
However, several previous works have found that the imbalanced nature of software defective data can decrease the model performance.
In this paper, we discussed the issue of how to improve imbalanced data distribution in the context of SDP, which can benefit software defect prediction with the aim of finding better methods.
Firstly, a relative density was introduced to reflect the significance of each instance within its class, which is irrelevant to the scale of data distribution in feature space; hence, it can be more robust than the absolute distance information.
Secondly, a K-nearest-neighbors-based probability density estimation (KNN-PDE) alike strategy was utilised to calculate the relative density of each training instance.
Furthermore, the fuzzy memberships of sample were designed based on relative density in order to eliminate classification error coming from noise and outlier samples.
Finally, two algorithms were proposed to train software defect prediction models based on the weighted extreme learning machine.
This paper compared the proposed algorithms with traditional SDP methods on the benchmark data sets.
It was proved that the proposed methods have much better overall performance in terms of the measures including G-mean, AUC, and Balance.
The proposed algorithms are more robust and adaptive for SDP data distribution types and can more accurately estimate the significance of each instance and assign the identical total fuzzy coefficients for two different classes without considering the impact of data scale.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Zheng, Shang& Gai, Jinjing& Yu, Hualong& Zou, Haitao& Gao, Shang. 2020. Software Defect Prediction Based on Fuzzy Weighted Extreme Learning Machine with Relative Density Information. Scientific Programming،Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1209218
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Zheng, Shang…[et al.]. Software Defect Prediction Based on Fuzzy Weighted Extreme Learning Machine with Relative Density Information. Scientific Programming No. 2020 (2020), pp.1-18.
https://search.emarefa.net/detail/BIM-1209218
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Zheng, Shang& Gai, Jinjing& Yu, Hualong& Zou, Haitao& Gao, Shang. Software Defect Prediction Based on Fuzzy Weighted Extreme Learning Machine with Relative Density Information. Scientific Programming. 2020. Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1209218
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1209218
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