Xiao Yu

TL;DR: A novel balanced distribution adaptation (BDA) based transfer learning method that simultaneously considers the two kinds of distribution differences and adaptively assigns different weights to them and achieves average improvements over four datasets.

TL;DR: A cost-sensitive ranking support vector machine (SVM) (CSRankSVM), which modifies the loss function of the ranking SVM algorithm by adding two penalty parameters to address both the cost issue and the data imbalance problem in RODP methods and achieves better performance.

TL;DR: This paper explores the potential of using resampling techniques and ensemble learning techniques to learn from imbalanced defect data for predicting the number of defects, and proposes two novel hybrid resamplings/boosting algorithms, called SmoteNDBoost and RusNDBoost, which introduce Smote ND andRusND into the AdaBoost.

TL;DR: A novel semi-supervised clustering-based data filtering method (i.e., SSDBSCAN filter) to filter out irrelevant cross-company defect prediction data and introduces multi-source TrAdaBoost algorithm, an effective transfer learning method, into CCDP to import knowledge not from one but from multiple sources to avoid negative transfer.