articleTop 1% cited
Feature selection based on artificial bee colony and gradient boosting decision tree
Applied Soft Computing · 2018 · Vol. 74 · pp. 634–642
Haidi Rao(Anhui Agricultural University)Xianzhang Shi(Anhui Agricultural University)Ahoussou Kouassi Rodrigue(Anhui Agricultural University)Juanjuan Feng(Anhui Agricultural University)Yingchun Xia(Anhui Agricultural University)Mohamed Elhoseny(Mansoura University)Xiaohui Yuan✉(University of North Texas)Lichuan Gu✉(Anhui Agricultural University)
Face and Expression RecognitionMachine Learning and Data ClassificationSpectroscopy and Chemometric AnalysesComputer scienceDecision treeFeature selectionBoosting (machine learning)Curse of dimensionalityArtificial intelligenceData miningMachine learningGradient boostingArtificial bee colony algorithm
Funding
- National Natural Science Foundation of China
Citations
568
FWCI
23.30
field-weighted impact
References
42
Percentile
100%
vs. same field & year
Citations per year
References
Greedy function approximation: A gradient boosting machine.
The Annals of Statistics · 2001 · 27,794 citations
Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy
IEEE Transactions on Pattern Analysis and Machine Intelligence · 2005 · 10,286 citations
Whale optimization approaches for wrapper feature selection
Applied Soft Computing · 2017 · 800 citations
Feature Selection
ACM Computing Surveys · 2017 · 2,235 citations
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