Journal ArticleDOI
Software defect prediction using a cost sensitive decision forest and voting, and a potential solution to the class imbalance problem
Michael J. Siers,Zahidul Islam +1 more
TLDR
Initial experimental results indicate a clear superiority of the proposed techniques over the existing methods for SDP*, a cost-sensitive decision forest and voting technique which is an ensemble of decision trees.About:
This article is published in Information Systems.The article was published on 2015-07-01. It has received 129 citations till now. The article focuses on the topics: Decision tree & Random forest.read more
Citations
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Journal ArticleDOI
Learning from imbalanced data: open challenges and future directions
TL;DR: Seven vital areas of research in this topic are identified, covering the full spectrum of learning from imbalanced data: classification, regression, clustering, data streams, big data analytics and applications, e.g., in social media and computer vision.
Journal ArticleDOI
An under‐sampled software defect prediction method based on hybrid multi‐objective cuckoo search
TL;DR: A hybrid multi‐objective cuckoo search under‐sampled software defect prediction model based on SVM (HMOCS‐US‐SVM) is proposed to solve synchronously above two problems of class imbalance in datasets and parameter selection of Support Vector Machine.
Journal ArticleDOI
Software defect prediction using stacked denoising autoencoders and two-stage ensemble learning
Haonan Tong,Bin Liu,Shihai Wang +2 more
TL;DR: A novel SDP approach which takes advantages of SDAEs and ensemble learning, namely the proposed two-stage ensemble (TSE), which concludes that deep representations are promising for SDP compared with traditional software metrics and TSE is more effective for addressing the class-imbalance problem in SDP.
Journal ArticleDOI
Software defect prediction based on kernel PCA and weighted extreme learning machine
Zhou Xu,Zhou Xu,Jin Liu,Jin Liu,Xiapu Luo,Zijiang Yang,Yifeng Zhang,Peipei Yuan,Yutian Tang,Tao Zhang +9 more
TL;DR: KPWE, a new software defect prediction framework that considers the feature extraction and class imbalance issues, is proposed, and the empirical study on 44 software projects indicate that KPWE is superior to the baseline methods in most cases.
Journal ArticleDOI
Class Imbalance Ensemble Learning Based on the Margin Theory
TL;DR: A novel ensemble margin based algorithm is proposed, which handles imbalanced classification by employing more low margin examples which are more informative than high margin samples, and compares the performances of different ensemble margin definitions in class imbalance learning.
References
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Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Journal ArticleDOI
SMOTE: synthetic minority over-sampling technique
TL;DR: In this article, a method of over-sampling the minority class involves creating synthetic minority class examples, which is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
Journal ArticleDOI
Bagging predictors
TL;DR: Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy.
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A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
Yoav Freund,Robert E. Schapire +1 more
TL;DR: The model studied can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting, and it is shown that the multiplicative weight-update Littlestone?Warmuth rule can be adapted to this model, yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems.
Journal ArticleDOI
SMOTE: Synthetic Minority Over-sampling Technique
TL;DR: In this article, a method of over-sampling the minority class involves creating synthetic minority class examples, which is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.