LERS
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.
Keywords for this software
References in zbMATH (referenced in 93 articles )
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- Dai, Jianhua; Xu, Qing: Approximations and uncertainty measures in incomplete information systems (2012)
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- Błaszczyński, Jerzy; Słowiński, Roman; Szeląg, Marcin: Sequential covering rule induction algorithm for variable consistency rough set approaches (2011)
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