| Software defect prediction using a cost sensitive decision forest and voting, and a potential solution to the class imbalance problem MJ Siers, MZ Islam Information Systems 51, 62-71, 2015 | 187 | 2015 |
| Novel algorithms for cost-sensitive classification and knowledge discovery in class imbalanced datasets with an application to NASA software defects MJ Siers, MZ Islam Information Sciences 459, 53-70, 2018 | 40 | 2018 |
| Cost sensitive decision forest and voting for software defect prediction MJ Siers, MZ Islam PRICAI 2014: Trends in Artificial Intelligence: 13th Pacific Rim …, 2014 | 23 | 2014 |
| Class imbalance and cost-sensitive decision trees: A unified survey based on a core similarity MJ Siers, MZ Islam ACM Transactions on Knowledge Discovery from Data (TKDD) 15 (1), 1-31, 2020 | 16 | 2020 |
| Addressing Class Imbalance and Cost Sensitivity in Software Defect Prediction by Combining Domain Costs and Balancing Costs MJ Siers, MZ Islam Advanced Data Mining and Applications, 2016 | 10 | 2016 |
| Standoff-balancing: A novel class imbalance treatment method inspired by military strategy MJ Siers, MZ Islam AI 2015: Advances in Artificial Intelligence: 28th Australasian Joint …, 2015 | 6 | 2015 |
| RBClust: High quality class-specific clustering using rule-based classification MJ Siers, MZ Islam European Symposium on Artificial Neural Networks, Computational Intelligence …, 2016 | 4 | 2016 |
| Cost-Sensitive Decision Forest: CSForest MJ Siers, MZ Islam | 1 | 2015 |
| Data science for class imbalanced and cost-sensitive data and its application to software defect prediction M Siers | | 2019 |
| WaterDM: A Knowledge Discovery and Decision Support Tool for Efficient Dam Management MZ Islam, M Furner, MJ Siers The 14th Australasian Data Mining Conference: AusDM 2016, 1-5, 2016 | | 2016 |
Professor Zahid IslamAssociate Dean (Research), Charles Sturt UniversityVerified email at csu.edu.au
