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A greedy feature selection algorithm for Big Data of high dimensionality
We present the Parallel, Forward–Backward with Pruning (PFBP) algorithm for feature selection (FS) for Big Data of high dimensionality. PFBP partitions the data matrix both in terms of rows as well as columns. By employing the concepts of p-values of conditional independence tests and meta-analysis...
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| Pubblicato in: | Mach Learn |
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| Autori principali: | , , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Springer US
2018
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6399683/ https://ncbi.nlm.nih.gov/pubmed/30906113 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1007/s10994-018-5748-7 |
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