BoosTexter: A boosting-based system for text categorization. This work focuses on algorithms which learn from examples to perform multiclass text and speech categorization tasks. Our approach is based on a new and improved family of boosting algorithms. We describe in detail an implementation, called BoosTexter, of the new boosting algorithms for text categorization tasks. We present results comparing the performance of BoosTexter and a number of other text-categorization algorithms on a variety of tasks. We conclude by describing the application of our system to automatic call-type identification from unconstrained spoken customer responses.

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  1. Huang, Ming; Zhuang, Fuzhen; Zhang, Xiao; Ao, Xiang; Niu, Zhengyu; Zhang, Min-Ling; He, Qing: Supervised representation learning for multi-label classification (2019)
  2. Zhang, Fengyi; Liao, Zhigao; Hu, Hongping: Application of multi-input Hamacher-ANFIS ensemble model on stock price forecast (2019)
  3. Li, Qian; Li, Gang; Niu, Wenjia; Cao, Yanan; Chang, Liang; Tan, Jianlong; Guo, Li: Boosting imbalanced data learning with Wiener process oversampling (2017)
  4. Wu, Yu-Ping; Lin, Hsuan-Tien: Progressive random (k)-labelsets for cost-sensitive multi-label classification (2017)
  5. Díez, Jorge; del Coz, Juan José; Luaces, Oscar; Bahamonde, Antonio: Using tensor products to detect unconditional label dependence in multilabel classifications (2016)
  6. Li, Hua; Li, Deyu; Zhai, Yanhui; Wang, Suge; Zhang, Jing: A novel attribute reduction approach for multi-label data based on rough set theory (2016)
  7. Meng, Jun; Wekesa, Jael-Sanyanda; Shi, Guan-Li; Luan, Yu-Shi: Protein function prediction based on data fusion and functional interrelationship (2016)
  8. Xu, Jianhua: Multi-label Lagrangian support vector machine with random block coordinate descent method (2016)
  9. Masnadi-Shirazi, Hamed; Vasconcelos, Nuno: A view of margin losses as regularizers of probability estimates (2015)
  10. Najmi, Erfan; Hashmi, Khayyam; Malik, Zaki; Rezgui, Abdelmounaam; Khan, Habib Ullah: CAPRA: a comprehensive approach to product ranking using customer reviews (2015) ioport
  11. Streich, Andreas P.; Buhmann, Joachim M.: Asymptotic analysis of estimators on multi-label data (2015)
  12. Su, Hongyu; Rousu, Juho: Multilabel classification through random graph ensembles (2015)
  13. Zimek, Arthur; Vreeken, Jilles: The blind men and the elephant: on meeting the problem of multiple truths in data from clustering and pattern mining perspectives (2015)
  14. Chekina, Lena; Gutfreund, Dan; Kontorovich, Aryeh; Rokach, Lior; Shapira, Bracha: Exploiting label dependencies for improved sample complexity (2013)
  15. Gao, Wei; Zhou, Zhi-Hua: On the consistency of multi-label learning (2013)
  16. Liu, Huawen; Zheng, Zhonglong; Zhao, Jianmin; Ye, Ronghua: An ensemble method for high-dimensional multilabel data (2013)
  17. Yu, Ying; Pedrycz, Witold; Miao, Duoqian: Neighborhood rough sets based multi-label classification for automatic image annotation (2013)
  18. Zhang, Tianzhu; Liu, Si; Xu, Changsheng; Lu, Hanqing: (\mathrmM^4\mathrmL): maximum margin multi-instance multi-cluster learning for scene modeling (2013)
  19. Bshouty, Nader H.; Long, Philip M.: Linear classifiers are nearly optimal when hidden variables have diverse effects (2012)
  20. Dembczyński, Krzysztof; Waegeman, Willem; Cheng, Weiwei; Hüllermeier, Eyke: On label dependence and loss minimization in multi-label classification (2012)

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