Weighted Indication-Based Similar Drug Sensing

Xu, Zheng; Zhu, Guangli; He, Congna; Zhang, Shunxiang; Du, Yanyong

doi:10.4018/ijssci.2015010105

Cited by 2 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The F1-measure can combine them to evaluate models, F1-measure is widely used in performance evaluation of prediction model and other software engineering research fields [12]. The calculation formula of F1-measure is the harmonic mean of recall and precision:…”

Section: Tp Precisionmentioning

confidence: 99%

“…With the development of theory and application in Sparse Representation based Classification (SRC), it has been widely applied in pattern recognition, image classification, defect prediction, and so on [10][11] [12]. Compared with the traditional classification algorithms, SRC has better discrimination and robustness, it consists of three parts: one is over-complete dictionary; the second is the linear representation under a specific sparse constraints based on the over-complete dictionary; the third is the classification according to the over-complete dictionary and the sparse coefficient of the input data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Software Defect Prediction Using Dictionary Learning

Wan¹,

Wu²,

Cheng³

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

View full text Add to dashboard Cite

Abstract-With the popularization of software version control system and defect tracking tools, large amounts of software development data is recorded. How to effectively use these data to improve the quality of software development, has become a hot topic in recent years. Software defect prediction technology can take full advantage of the historical data to build predictive models and automatically detect defective modules for efficient software test to improve the quality of a software system. But the class-imbalanced data makes the prediction model classifying a modules as a defective-free one easily, while the misclassification of defective modules generally incurs much higher cost risk than the misclassification of defective-free ones. To resolve this problem, we propose a cost-sensitive software defect prediction method using dictionary learning. It iteratively optimizes the classifier parameters and the dictionary atoms, to ensure that the extracted features (sparse representation) are optimal for the trained classifier; Moreover, we take the different misclassification costs into account, increasing the punishment on misclassification defective modules in the procedure of dictionary learning, making classification inclining to classify a module as a defective one. Experimental results on the 10 classimbalanced data sets of NASA show that our method is more effective than other methods.

show abstract

Section: Tp Precisionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%