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.

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  1. Amin, Talha; Moshkov, Mikhail: Totally optimal decision rules (2018)
  2. Chen, Yumin; Wu, Keshou; Chen, Xuhui; Tang, Chaohui; Zhu, Qingxin: An entropy-based uncertainty measurement approach in neighborhood systems (2014)
  3. Clark, Patrick G.; Grzymala-Busse, Jerzy W.; Rzasa, Wojciech: Mining incomplete data with singleton, subset and concept probabilistic approximations (2014)
  4. Du, Gengshen; Ruhe, Guenther: Two machine-learning techniques for mining solutions of the ReleasePlanner$^\text TM$ decision support system (2014) ioport
  5. Liu, Dun; Li, Tianrui; Zhang, Junbo: A rough set-based incremental approach for learning knowledge in dynamic incomplete information systems (2014)
  6. Yeh, Ching-Chiang; Chi, Der-Jang; Lin, Yi-Rong: Going-concern prediction using hybrid random forests and rough set approach (2014) ioport
  7. Deckert, Magdalena: Incremental rule-based learners for handling concept drift: an overview (2013) ioport
  8. Grzymala-Busse, Jerzy W.; Hippe, Zdzislaw S.: Mining incomplete data--A rough set approach (2013) ioport
  9. Sakai, Hiroshi; Okuma, Hitomi; Nakata, Michinori: Rough non-deterministic information analysis: foundations and its perspective in machine learning (2013) ioport
  10. Yao, Yiyu; Deng, Xiaofei: A granular computing paradigm for concept learning (2013) ioport
  11. Zhang, Xianyong; Miao, Duoqian: Two basic double-quantitative rough set models of precision and grade and their investigation using granular computing (2013)
  12. Dai, Jianhua; Xu, Qing: Approximations and uncertainty measures in incomplete information systems (2012)
  13. Hu, Feng; Wang, Guoyin: Knowledge reduction based on divide and conquer method in rough set theory (2012)
  14. Błaszczyński, Jerzy; Słowiński, Roman; Szeląg, Marcin: Sequential covering rule induction algorithm for variable consistency rough set approaches (2011) ioport
  15. Sakai, Hiroshi; Hayashi, Kohei; Nakata, Michinori; Ślȩzak, Dominik: A mathematical extension of rough set-based issues toward uncertain information analysis (2011)
  16. Sakai, Hiroshi; Okuma, Hitomi; Nakata, Michinori; Ślȩzak, Dominik: Stable rule extraction and decision making in rough non-deterministic information analysis (2011)
  17. Szkoła, Jarosław; Pancerz, Krzysztof; Warchoł, Jan: Recurrent neural networks in computer-based clinical decision support for laryngopathies: an experimental study (2011) ioport
  18. Chen, You-Shyang; Cheng, Ching-Hsue: Forecasting PGR of the financial industry using a rough sets classifier based on attribute-granularity (2010) ioport
  19. Dembczyński, Krzysztof; Kotłowski, Wojciech; Słowiński, Roman: ENDER: a statistical framework for boosting decision rules (2010) ioport
  20. Grzymala-Busse, Jerzy W.; Grzymala-Busse, Witold J.; Hippe, Zdzisław S.; Rząsa, Wojciech: An improved comparison of three rough set approaches to missing attribute values (2010) ioport

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