Baowen Xu focuses on Data mining, Artificial intelligence, Machine learning, Software and Object-oriented programming. His Data mining research is multidisciplinary, relying on both Context, Class size, Test case, Range and Regression testing. The concepts of his Artificial intelligence study are interwoven with issues in Software quality, Set, Metric and Pattern recognition.
His Machine learning research is multidisciplinary, incorporating perspectives in Computational linguistics, Metamorphic testing and Oracle. The study incorporates disciplines such as Statistics, Cohesion, Cyclomatic complexity and Slicing in addition to Object-oriented programming. His study looks at the relationship between Test Management Approach and fields such as Task, as well as how they intersect with chemical problems.
His primary scientific interests are in Data mining, Artificial intelligence, Programming language, Software and Machine learning. The Data mining study combines topics in areas such as Context, Modified condition/decision coverage, Test case, Code coverage and Empirical research. His research in Test case intersects with topics in Algorithm, Regression testing and Test Management Approach.
His work deals with themes such as Metric and Pattern recognition, which intersect with Artificial intelligence. His study ties his expertise on Slicing together with the subject of Programming language. His Software research incorporates themes from Debugging, Software engineering and Source code.
His primary areas of investigation include Artificial intelligence, Machine learning, Software, Software bug and Data mining. His Artificial intelligence research incorporates elements of Baseline and Code. His studies in Machine learning integrate themes in fields like Test data, Context, Task and Data modeling.
His biological study spans a wide range of topics, including Python, Function and Upstream, Downstream. His Software bug research is multidisciplinary, incorporating elements of Cross project, Software metric, Maintenance engineering, Workaround and Empirical research. His Data mining study which covers Metric that intersects with Sparse approximation and Obfuscation.
His scientific interests lie mostly in Software, Data mining, Artificial intelligence, Machine learning and Software bug. His work in the fields of Analysis effort method overlaps with other areas such as Effort management. His Data mining study combines topics from a wide range of disciplines, such as Estimation, Metric, Estimator, Empirical research and AdaBoost.
In general Artificial intelligence study, his work on Kernel, Ensemble learning and Kernel often relates to the realm of Multiple kernel learning, thereby connecting several areas of interest. His study in the fields of Feature vector under the domain of Machine learning overlaps with other disciplines such as Stage. His Python study also includes
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An Efficient Identity-Based Conditional Privacy-Preserving Authentication Scheme for Vehicular Ad Hoc Networks
Debiao He;Sherali Zeadally;Baowen Xu;Xinyi Huang.
IEEE Transactions on Information Forensics and Security (2015)
Link prediction in social networks: the state-of-the-art
Peng Wang;Peng Wang;BaoWen Xu;BaoWen Xu;BaoWen Xu;YuRong Wu;XiaoYu Zhou.
Science in China Series F: Information Sciences (2015)
A brief survey of program slicing
Baowen Xu;Ju Qian;Xiaofang Zhang;Zhongqiang Wu.
ACM Sigsoft Software Engineering Notes (2005)
A novel ensemble method for classifying imbalanced data
Zhongbin Sun;Qinbao Song;Xiaoyan Zhu;Heli Sun.
Pattern Recognition (2015)
A theoretical analysis of the risk evaluation formulas for spectrum-based fault localization
Xiaoyuan Xie;Tsong Yueh Chen;Fei-Ching Kuo;Baowen Xu.
formal methods (2013)
Testing and validating machine learning classifiers by metamorphic testing
Xiaoyuan Xie;Joshua W. K. Ho;Christian Murphy;Gail Kaiser.
international conference on quality software (2011)
Super-resolution Person re-identification with semi-coupled low-rank discriminant dictionary learning
Xiao-Yuan Jing;Xiaoke Zhu;Fei Wu;Xinge You.
computer vision and pattern recognition (2015)
Heterogeneous cross-company defect prediction by unified metric representation and CCA-based transfer learning
Xiaoyuan Jing;Fei Wu;Xiwei Dong;Fumin Qi.
foundations of software engineering (2015)
Effort-aware just-in-time defect prediction: simple unsupervised models could be better than supervised models
Yibiao Yang;Yuming Zhou;Jinping Liu;Yangyang Zhao.
foundations of software engineering (2016)
Examining the Potentially Confounding Effect of Class Size on the Associations between Object-Oriented Metrics and Change-Proneness
Yuming Zhou;H. Leung;Baowen Xu.
IEEE Transactions on Software Engineering (2009)
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