@inproceedings{9f789a9df65f4747a3731fc312cc4e8a,
title = "Cross-Project Software Defect Prediction Based on Feature Selection and Transfer Learning",
abstract = "Cross-project software defect prediction solves the problem that traditional defect prediction can{\textquoteright}t get enough data, but how to apply the model learned from the data of different mechanisms to the target data set is a new problem. At the same time, there is the problem that information redundancy in the training process leads to low accuracy. Based on the difference of projects, this paper uses MIC to filter features to solve the problem of information redundancy. At the same time, combined with the TrAdaboost algorithm, which is based on the idea of aggravating multiple classification error samples, this paper proposes a cross-project software prediction method based on feature selection and migration learning. Experimental results show that the algorithm proposed in this paper has better experimental results on AUC and F1.",
keywords = "Cross-project software defect prediction, MIC, TrAdaboost, Transfer learning",
author = "Tianwei Lei and Jingfeng Xue and Weijie Han",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020 ; Conference date: 08-10-2020 Through 10-10-2020",
year = "2020",
doi = "10.1007/978-3-030-62463-7_33",
language = "English",
isbn = "9783030624620",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "363--371",
editor = "Xiaofeng Chen and Hongyang Yan and Qiben Yan and Xiangliang Zhang",
booktitle = "Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings",
address = "Germany",
}