Presumably correct decision sets
نویسندگان
چکیده
The paper presents the presumably correct decision sets as a tool to analyze uncertainty in form of inconsistency systems. As first step, problem instances are gathered into three regions containing weak members, borderline and strong members. This is accomplished by using membership degrees their neighborhoods while neglecting actual labels. second we derive incorrect contrasting classes determined neighborhood function with classes. We extract these from either members or whole universe, which defines strict relaxed versions our theoretical formalism. These allow isolating difficult handle machine learning algorithms they responsible for inconsistent patterns. simulations synthetic real-world datasets illustrate advantages model compared rough sets, deemed solid state-of-the-art approach cope inconsistency. In particular, it shown that can increase accuracy selected classifiers up 36% weighting during training process.
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Article history: Available online 23 March 2013
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ژورنال
عنوان ژورنال: Pattern Recognition
سال: 2023
ISSN: ['1873-5142', '0031-3203']
DOI: https://doi.org/10.1016/j.patcog.2023.109640