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A Robust AUC Maximization Framework With Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification
The positive-unlabeled (PU) classification is a common scenario in real-world applications such as healthcare, text classification, and bioinformatics, in which we only observe a few samples labeled as “positive” together with a large volume of “unlabeled” samples that may contain both positive and...
Uloženo v:
| Vydáno v: | IEEE Trans Neural Netw Learn Syst |
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| Hlavní autoři: | , , , , , , , |
| Médium: | Artigo |
| Jazyk: | Inglês |
| Vydáno: |
2018
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| Témata: | |
| On-line přístup: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7416499/ https://ncbi.nlm.nih.gov/pubmed/30307881 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/TNNLS.2018.2870666 |
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