Journal Article10.1016/J.CHEMOLAB.2017.12.014
Bagging classification tree-based robust variable selection for radial basis function network modeling in metabonomics data analysis
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TL;DR: The results showed that BAGCT-RBFN can find a shortlist of discriminatory variables with reliability while attain more satisfactory classification accuracy than traditional CT and RBFN.
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About: This article is published in Chemometrics and Intelligent Laboratory Systems. The article was published on 15 Mar 2018. The article focuses on the topics: Radial basis function network.
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Citations
iRSpot-SPI: Deep learning-based recombination spots prediction by incorporating secondary sequence information coupled with physio-chemical properties via Chou's 5-step rule and pseudo components
TL;DR: A deep neural network is proposed to predict recombination spots by fusing both the secondary sequence information and physio-chemical derived features and it is anticipated, that this model will provide novel insight into basic research, drug designing, academic research and recombinations spots studies particularly.
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Cancer Classification with a Cost-Sensitive Naive Bayes Stacking Ensemble.
TL;DR: Wang et al. as discussed by the authors used fast correlation-based feature selection (FCBF) method to preprocess the data to eliminate irrelevant and redundant features, and then the classification was carried out in the stacking ensemble learner.
A Filter Based Improved Decision Tree Sentiment Classification Model for RealTime Amazon Product Review Data
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TL;DR: The proposed filter-based improved decision tree sentiment classification model for real-time amazon product review data recommends the product based on the user query by prediction using a new novel normalized product review sentiment score and ranked feature selection measure.
Fast and non-destructive discriminating the geographical origin of Hangbaiju by hyperspectral imaging combined with chemometrics.
Wanjun Long,Qi Zhang,Siyu Wang,Yixin Suo,Hengye Chen,Xiuyun Bai,Xiaolong Yang,Yan Zhou,Jian Yang,Haiyan Fu +9 more
TL;DR: Zhang et al. as discussed by the authors used a bagging classification tree-radial basis function (BAGCT-RBFN) compared with classification tree (CT), radial basis function network (RBFN), was applied to discriminate Hangbaiju samples from different origins.
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Machine learning to predict the specific optical rotations of chiral fluorinated molecules
TL;DR: A chemoinformatics method was applied to the assignment of absolute configurations and to the quantitative prediction of specific optical rotations using a data set of 88 chiral fluorinated molecules (44 pairs of enantiomers) and counterpropagation neural networks were explored for the classification ofEnantiomers as dextrorotatory or levorotatory.
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