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• W1976072895 abstract "The performance of a classification system of any type can suffer from irrelevant or redundant data, contained in characteristic features that describe objects of the universe. To estimate relevance of attributes and select their subset for a constructed classifier typically either a filter, wrapper, or an embedded approach, is implemented. The paper presents a combined wrapper framework, where in a pre-processing step, a ranking of variables is established by a simple wrapper model employing sequential backward search procedure. Next, another predictor exploits this resulting ordering of features in their reduction. The proposed methodology is illustrated firstly for a binary classification task of authorship attribution from stylometric domain, and then for additional verification for a waveform dataset from UCI machine learning repository." @default.
• W1976072895 created "2016-06-24" @default.
• W1976072895 creator A5010393120 @default.
• W1976072895 date "2014-06-11" @default.
• W1976072895 modified "2023-09-24" @default.
• W1976072895 title "Ranking of characteristic features in combined wrapper approaches to selection" @default.
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• W1976072895 doi "https://doi.org/10.1007/s00521-014-1620-2" @default.
• W1976072895 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4305374" @default.
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