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- W1596697452 abstract "Learning from examples consists of knowledge induction from training examples using an inductive learning algorithm. In practice, a preprocessing phase is necessary to transform numerical attributes into intervals (discretization), to deal with missing values, noisy data, and so forth. The major part of research efforts has proposed methods for data pre-processing but no special attention has been devoted to model and to handle the uncertainty inherent in real-world data. In this paper we introduce a preprocessing task to model uncertainty which is considered during all the learning process. In this context we formalize a notion of flexible concepts in contrast with “sharp” concepts usually represented by crisp sets. Next, we discuss an inductive learning approach based on rough set theory and we propose a method which allows the user to control both the granularity of knowledge resulting from the partition of the universe and the consistency of rules to be learned. Our proposed method is at the basis of the system Alpha which is run on real-world datasets." @default.
- W1596697452 created "2016-06-24" @default.
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- W1596697452 date "1997-01-01" @default.
- W1596697452 modified "2023-10-02" @default.
- W1596697452 title "Learning flexible concepts from uncertain data" @default.
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- W1596697452 doi "https://doi.org/10.1007/3-540-63614-5_49" @default.
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