Please use this identifier to cite or link to this item:
|Title:||Maximum entropy discrimination poisson regression for software reliability modeling||Authors:||Chatzis, Sotirios P.
Andreou, Andreas S.
|Keywords:||Count data;Dirichlet process (DP);Max-margin;Mean-field;Software reliability||Category:||Computer and Information Sciences||Field:||Natural Sciences||Issue Date:||27-Jan-2015||Publisher:||IEEE||Source:||IEEE Transactions on Neural Networks and Learning Systems, 2015, Volume 26, Issue 11||Abstract:||Reliably predicting software defects is one of the most significant tasks in software engineering. Two of the major components of modern software reliability modeling approaches are: 1) extraction of salient features for software system representation, based on appropriately designed software metrics and 2) development of intricate regression models for count data, to allow effective software reliability data modeling and prediction. Surprisingly, research in the latter frontier of count data regression modeling has been rather limited. More specifically, a lack of simple and efficient algorithms for posterior computation has made the Bayesian approaches appear unattractive, and thus underdeveloped in the context of software reliability modeling. In this paper, we try to address these issues by introducing a novel Bayesian regression model for count data, based on the concept of max-margin data modeling, effected in the context of a fully Bayesian model treatment with simple and efficient posterior distribution updates. Our novel approach yields a more discriminative learning technique, making more effective use of our training data during model inference. In addition, it allows of better handling uncertainty in the modeled data, which can be a significant problem when the training data are limited. We derive elegant inference algorithms for our model under the mean-field paradigm and exhibit its effectiveness using the publicly available benchmark data sets.||URI:||http://ktisis.cut.ac.cy/handle/10488/8198||ISSN:||2162-237X||DOI:||10.1109/TNNLS.2015.2391171||Rights:||© IEEE||Type:||Article|
|Appears in Collections:||Άρθρα/Articles|
Show full item record
checked on Dec 8, 2018
checked on Dec 13, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.