LERS – a system for learning from examples based on rough sets. The paper presents the system LERS for rule induction. The system handles inconsistencies in the input data due to its usage of rough set theory principle. Rough set theory is especially well suited to deal with inconsistencies. In this approach, inconsistencies are not corrected. Instead, system LERS computes lower and upper approximations of each concept. Then it induces certain rules and possible rules. The user has the choice to use the machine learning approach or the knowledge acquisition approach. In the first case, the system induces a single minimal discriminant description for each concept. In the second case, the system induces all rules, each in the minimal form, that can be induced from the input data. In both cases, the user has a choice between the local or global approach.

References in zbMATH (referenced in 117 articles )

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  1. Campagner, Andrea; Cabitza, Federico; Ciucci, Davide: The three-way-in and three-way-out framework to treat and exploit ambiguity in data (2020)
  2. Amin, Talha; Moshkov, Mikhail: Totally optimal decision rules (2018)
  3. Qian, Yuhua; Liang, Xinyan; Wang, Qi; Liang, Jiye; Liu, Bing; Skowron, Andrzej; Yao, Yiyu; Ma, Jianmin; Dang, Chuangyin: Local rough set: a solution to rough data analysis in big data (2018)
  4. 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)
  5. Yao, Yiyu: Rough-set concept analysis: interpreting RS-definable concepts based on ideas from formal concept analysis (2016)
  6. Cheng, Ching-Hsue; Wang, Ssu-Hsiang: A quarterly time-series classifier based on a reduced-dimension generated rules method for identifying financial distress (2015)
  7. Chen, Jinkun; Lin, Yaojin; Lin, Guoping; Li, Jinjin; Ma, Zhouming: The relationship between attribute reducts in rough sets and minimal vertex covers of graphs (2015)
  8. Chen, Yumin; Wu, Keshou; Chen, Xuhui; Tang, Chaohui; Zhu, Qingxin: An entropy-based uncertainty measurement approach in neighborhood systems (2014)
  9. Clark, Patrick G.; Grzymala-Busse, Jerzy W.; Rzasa, Wojciech: Mining incomplete data with singleton, subset and concept probabilistic approximations (2014)
  10. Du, Gengshen; Ruhe, Guenther: Two machine-learning techniques for mining solutions of the ReleasePlanner(^\textTM) decision support system (2014) ioport
  11. Fan, Bingjiao; Xu, Weihua; Yu, Jianhang: Uncertainty measures in ordered information system based on approximation operators (2014)
  12. Liu, Dun; Li, Tianrui; Zhang, Junbo: A rough set-based incremental approach for learning knowledge in dynamic incomplete information systems (2014)
  13. Yeh, Ching-Chiang; Chi, Der-Jang; Lin, Yi-Rong: Going-concern prediction using hybrid random forests and rough set approach (2014) ioport
  14. Deckert, Magdalena: Incremental rule-based learners for handling concept drift: an overview (2013) ioport
  15. Grzymala-Busse, Jerzy W.; Hippe, Zdzislaw S.: Mining incomplete data--A rough set approach (2013) ioport
  16. Hu, Yi-Chung: Rough sets for pattern classification using pairwise-comparison-based tables (2013)
  17. Sakai, Hiroshi; Okuma, Hitomi; Nakata, Michinori: Rough non-deterministic information analysis: foundations and its perspective in machine learning (2013) ioport
  18. Yao, Yiyu; Deng, Xiaofei: A granular computing paradigm for concept learning (2013) ioport
  19. Zhang, Xianyong; Miao, Duoqian: Two basic double-quantitative rough set models of precision and grade and their investigation using granular computing (2013)
  20. Dai, Jianhua; Xu, Qing: Approximations and uncertainty measures in incomplete information systems (2012)

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