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Bayesian Confirmation Measures in Rule-Based Classification
2017With the rapid growth of available data, learning models are also gaining in sizes. As a result, end-users are often faced with classification results that are hard to understand. This problem also involves rule-based classifiers, which usually concentrate on predictive accuracy and produce too many rules for a human expert to interpret. In this paper,
Dariusz Brzezinski +2 more
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Classification and approximation with rule-based networks
1993This thesis describes the architecture of learning systems which can explain their decisions through a rule-based knowledge representation. Two problems in learning are addressed: pattern classification and function approximation. In Part I, a pattern classifier for discrete-valued problems is presented.
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Expressive and explainable rule-based classification
Classification rule learning produces expressive rules so that a human user can easily interpret the rationale behind the predictions of the generated model. Constructing a very accurate classification model may lead to overfitting, a common problem in data mining that causes a leaner to perform badly on test instances.openaire +1 more source
A Generic Framework for Rule-Based Classification
2008Classification is an important field of data mining problems. Given a set of labeled training examples the classification task constructs a classifier. A classifier is a global model which is used to predict the class label for data objects that are unlabeled. Many approaches have been proposed for the classification problem. Among them, rule-induction,
Giacometti, Arnaud +3 more
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New approach to cancer therapy based on a molecularly defined cancer classification
Ca-A Cancer Journal for Clinicians, 2014Javier Cortes +2 more
exaly
Effect of rule weights in fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems, 2001Hisao Ishibuchi
exaly

