In this paper, we propose a new approach of classification based on rough sets denoted Dynamic Belief Rough Set Classifier (D-BRSC) which is able to learn decision rules from uncertain data. The uncertainty appears only in decision attributes and is ...
This tutorial gives an introduction to Learning Classifier Systems focusing on the Michigan-Style type and XCS in particular. The objective is to introduce (1) where LCSs come from, (2) how LCSs generally work, (3) which different systems exist, (4) how ...
This paper presents adaptive boosting with uncertainty-based selective sampling (AdaBUS), a variant of the AdaBoost algorithm for boosting the Nai@?ve Bayes (NB) text classification. Although the boosting technique has been shown to effectively improve ...
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