The optimal combination of text representations and classifiers for different-size text sets.

<p>The optimal combination of text representations and classifiers for different-size text sets.</p>

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Bibliographic Details
Main Author:	Lu Xiao (314030) (author)
Other Authors:	Qiaoxing Li (730432) (author), Qian Ma (69668) (author), Jiasheng Shen (19871113) (author), Yong Yang (63842) (author), Danyang Li (1490554) (author)
Published:	2024
Subjects:	Genetics Cancer Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified public opinion monitoring knn ), known uneven text length 29 %, 5 important research area level tourist attractions text classification tends scale text data class text classification word2vec </ p optimizing traditional tf identify rare categories tourist attractions subcategories specific category performance text classification algorithm tourist attractions text set text mining text datasets category discriminability big data 55 %, six subcategories related research multiple subcategories good performance vector expression useful reference true value specific fields results demonstrate proposed algorithm professional dataset predicted value pos weighting modified tf model stability managing large imbalanced dataset even outperforms efficiently implement different sizes conclusions obtained beneficial exploration algorithm presented achieve multi
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