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008 210208s2020 ua d f m 000 0 eng d
040 _aEG-GiCUC
_beng
_cEG-GiCUC
041 0 _aeng
049 _aDeposite
097 _aM.Sc
099 _aCai01.13.10.M.Sc.2020.Mo.S
100 0 _aMoheb Mofied Ragheb Henein
245 1 0 _aSoftware defect prediction using data categorization and machine learning techniques /
_cMoheb Mofied Ragheb Henein ; Supervised Salwa K. Abdelhafiz , Doaa M. Shawky
246 1 5 _aالتنبؤ بعيوب البرامج عن طريق استخدام طرق تصنيف البيانات وتقنيات التعلم الآلى
260 _aCairo :
_bMoheb Mofied Ragheb Henein ,
_c2020
300 _a81 P . :
_bcharts ;
_c30cm
502 _aThesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Mathematics and Physics
520 _aIn this thesis, two approaches are proposed to overcome two main challenges in SDP; namely the class imbalance and overlap. The first approach is Clustering-based Undersampling Artificial Neural Network (CU-ANN) that tackles the imbalance problem. The second approach is Hybrid sampling Cost- Sensitive Support Vector Machine (HCSVM), which balances the data set by undersampling the majority class instances and oversampling the minority class ones. Moreover, minority samples are categorized based on their severity, where the degree of severity is directly proportional to the number of neighbors belonging to the majority class. Taking into consideration the severity of minority samples in the learning phase alleviates the impact of class overlap. A cost-sensitive approach that assigns high misclassification costs to di cult minority samples considers these samples rather than treating them as outliers. Experiments are conducted on benchmark data sets, NASA MDP, which are the most used datasets in SDP performance evaluation
530 _aIssued also as CD
653 4 _aArtificial Neural Network
653 4 _aDefect
653 4 _aSoftware Defect Prediction
700 0 _aDoaa M. Shawky ,
_eSupervisor
700 0 _aSalwa K. Abdelhafiz ,
_eSupervisor
856 _uhttp://172.23.153.220/th.pdf
905 _aAmira
_eCataloger
905 _aNazla
_eRevisor
942 _2ddc
_cTH
999 _c79849
_d79849