As one of the most promising classification schemes, association classification (AC)
effectively integrates classification with association rule discovery, but it requires precisely …

Rule-based classifier has shown its popularity in building many decision support systems
such as medical diagnosis and financial fraud detection. One major advantage is that the …

Mining class association rules is an important task for associative classification and plays a
key role in rule-based decision support systems. Most of the existing methods try the best to …

The associative classification method integrates association rule mining and classification.
Constructing an efficient classifier with a small set of high quality rules is a highly important …

Text classification is imperative in order to search for more accessible and appropriate
information. It utilized in various fields, including marketing, security, biomedical, etc. Apart …

Associative classifiers use association rules to associate attribute values with observed
class labels. This model has been recently introduced in the literature and shows good …

Soft set theory provides a parameterized treatment of uncertainty, which is closely related to
soft computing models like fuzzy sets and rough sets. Based on soft sets and logical …

The decision tree learning algorithms, eg, C5, are good at dataset classification. But those
algorithms usually work with only one attribute at a time. The dependencies among …

Association classification has been an important type of the rule-based classification. A
variety of approaches have been proposed to build a classifier based on classification rules …

The paper describes two soft induction techniques, GDT-NR and GDT-RS, for discovering
classification rules from databases with uncertainty and incompleteness. The techniques are …