BoosTexter

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.


References in zbMATH (referenced in 76 articles )

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  1. Che, Xiaoya; Chen, Degang; Mi, Jusheng: A novel approach for learning label correlation with application to feature selection of multi-label data (2020)
  2. Kocev, Dragi; Ceci, Michelangelo; Stepišnik, Tomaž: Ensembles of extremely randomized predictive clustering trees for predicting structured outputs (2020)
  3. Tan, Zhi-Hao; Tan, Peng; Jiang, Yuan; Zhou, Zhi-Hua: Multi-label optimal margin distribution machine (2020)
  4. Wu, Guoqiang; Zheng, Ruobing; Tian, Yingjie; Liu, Dalian: Joint ranking SVM and binary relevance with robust low-rank learning for multi-label classification (2020)
  5. Yang, Bo; Tong, Kunkun; Zhao, Xueqing; Pang, Shanmin; Chen, Jinguang: Multilabel classification using low-rank decomposition (2020)
  6. Huang, Ming; Zhuang, Fuzhen; Zhang, Xiao; Ao, Xiang; Niu, Zhengyu; Zhang, Min-Ling; He, Qing: Supervised representation learning for multi-label classification (2019)
  7. Ma, Jianghong; Chow, Tommy W. S.: Label-specific feature selection and two-level label recovery for multi-label classification with missing labels (2019)
  8. Zhang, Fengyi; Liao, Zhigao; Hu, Hongping: Application of multi-input Hamacher-ANFIS ensemble model on stock price forecast (2019)
  9. Liu, Yi; Luo, Yu; Zhu, Youwen; Liu, Yang; Li, Xingxin: Secure multi-label data classification in cloud by additionally homomorphic encryption (2018)
  10. Ma, Jianghong; Chow, Tommy W. S.: Robust non-negative sparse graph for semi-supervised multi-label learning with missing labels (2018)
  11. Li, Qian; Li, Gang; Niu, Wenjia; Cao, Yanan; Chang, Liang; Tan, Jianlong; Guo, Li: Boosting imbalanced data learning with Wiener process oversampling (2017)
  12. Wu, Yu-Ping; Lin, Hsuan-Tien: Progressive random (k)-labelsets for cost-sensitive multi-label classification (2017)
  13. Díez, Jorge; del Coz, Juan José; Luaces, Oscar; Bahamonde, Antonio: Using tensor products to detect unconditional label dependence in multilabel classifications (2016)
  14. 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)
  15. Meng, Jun; Wekesa, Jael-Sanyanda; Shi, Guan-Li; Luan, Yu-Shi: Protein function prediction based on data fusion and functional interrelationship (2016)
  16. Miratrix, Luke; Ackerman, Robin: Conducting sparse feature selection on arbitrarily long phrases in text corpora with a focus on interpretability (2016)
  17. Sergienko, Roman B.; Shan, Muhammad; Minker, Wolfgang; Semenkin, Eugene S.: Topic categorization based on collectives of term weighting methods for natural language call routing (2016)
  18. Xu, Jianhua: Multi-label Lagrangian support vector machine with random block coordinate descent method (2016)
  19. Masnadi-Shirazi, Hamed; Vasconcelos, Nuno: A view of margin losses as regularizers of probability estimates (2015)
  20. Najmi, Erfan; Hashmi, Khayyam; Malik, Zaki; Rezgui, Abdelmounaam; Khan, Habib Ullah: CAPRA: a comprehensive approach to product ranking using customer reviews (2015) ioport

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