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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Veröffentlicht in:	Machine learning Jg. 108; H. 2; S. 149 - 202
Hauptverfasser:	Tsamardinos, Ioannis, Borboudakis, Giorgos, Katsogridakis, Pavlos, Pratikakis, Polyvios, Christophides, Vassilis
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.02.2019 Springer Nature B.V Springer Verlag
Schlagworte:	Algorithms Artificial Intelligence Bayesian analysis Big Data Computer Science Computer simulation Control Data management Empirical analysis Greedy algorithms Iterative methods Machine Learning Mechatronics Natural Language Processing (NLP) Parallel processing Partitions Polymorphism Pruning Robotics Simulation and Modeling Big Data Feature selection Data analytics Forward selection Variable selection
ISSN:	0885-6125, 1573-0565
Online-Zugang:	Volltext
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