• No results found

Building an Ensemble for Software Defect Prediction Based on Diversity Selection

N/A
N/A
Protected

Academic year: 2021

Share "Building an Ensemble for Software Defect Prediction Based on Diversity Selection"

Copied!
10
0
0

Loading.... (view fulltext now)

Full text

Loading

Figure

Table 1: PROMISE data sets used in our experiment Id Data set Language # of modules
Figure 1: Stacking building
Table 3: Confusion matrix
Table 5: Relative increase in true positives of all techniques compared to Bagging
+2

References

Related documents

We also empirically examine whether the type of insider - CEOs versus other insiders, insider trade characteristics - opportunistic versus non-opportunistic, and insider sales

b Comparison of the number of proteins identified and quantified in control microglia and LPS-activated microglia derived extracellular vesicles samples.. c Bioinformatic analysis

It generally has been believed that the cause of his death was “liver cancer.” However, as indicated in the official autopsy report, dated March 13, 1925, of the Peking Union

The CMS Muon community decided to start an R&D activity to exploit machine learning and deep learning techniques for developing an innovative tool for monitoring the L1 muon

Methods: In this study, we found that a small molecule inhibitor of poly (ADP-ribose) polymerase (PARP), PJ-34, is very effective in activating S/G2M cell cycle checkpoints,

Relationship between solar wind dynamic pressure and amplitude of geomagnetic sudden commencement (SC) Araki and Shinbori Earth, Planets and Space (2016) 68 90 DOI 10 1186/s40623 016 0444

The u (top) and d (bottom) valence quark PDFs fitted on the combination of HERA-I DIS data and the CMS W-boson muon charge asymmetry measurement (filled line) are shown to- gether

 Katie and HLM have provided resources and information at many of our district health and wellness events including our Healthy Living Family series, additionally, provided