In this paper, we propose a new method for measuring the closeness of multiple sets of rules that are combined using Dempster’s rule of combination to improve classification performance. The closeness provides an insight into combining multiple sets of rules in classification − in what circumambience the performance of combinations of some sets of rules using Dempster’s rule is better than that...

The paper describes an inductive learning environment called DELVAUX for classiication tasks that learns PROSPECTOR-style, Bayesian rules from sets of examples. A genetic algorithm approach is used for learning Bayesian rule-sets, in which a population consists of sets of rule-sets that generate oosprings through the exchange of rules, permitting tter rule-sets to produce oosprings with a highe...

The discovery of association rule acquire an imperative role in data mining since its inception, which tries to find correlation among the attributes in a database. Classical algorithms/procedures meant for Boolean data and they suffer from sharp boundary problem in handling quantitative data. Thereby fuzzy association rule (i.e., association rule based on fuzzy sets) with fuzzy minimum support...

Association rules, one of the most useful constructs in data mining, can be exerted to capture interesting dependencies between variables in large datasets. Herawan and Deris initiated the investigation of mining association rules from transactional datasets using soft set theory. Unfortunately, some existing concepts in the literature were unable to realize properly Herawan and Deris’s initial...

Association rule algorithms often generate an excessive number of rules, many of which are not significant. It is difficult to determine which rules are more useful, interesting and important. We introduce a rough set based Rule Importance Measure to select the most important rules. We use ROSETTA software to generate multiple reducts. Apriori association rule algorithm is then applied to gener...

This presentation may look like a glossary of the fusion rules and we also introduce new ones presenting their formulas and examples: Conjunctive, Disjunctive, Exclusive Disjunctive, Mixed Conjunctive-Disjunctive rules, Conditional rule, Dempster's, Yager's, Smets' TBM rule, Dubois-Prade's, Dezert-Smarandache classical and hybrid rules, Murphy's average rule, Inagaki-Lefevre-Colot-Vannoorenberg...

This chapter may look like a glossary of the fusion rules and we also introduce new ones presenting their formulas and examples: Conjunctive, Disjunctive, Exclusive Disjunctive, Mixed Conjunctive-Disjunctive rules, Conditional rule, Dempster’s, Yager’s, Smets’ TBM rule, Dubois-Prade’s, DezertSmarandache classical and hybrid rules, Murphy’s average rule, Inagaki-LefevreColot-Vannoorenberghe Unif...

Humans are able to flexibly devise and implement rules to reach their desired goals. For simple situations, we can use single rules, such as "if traffic light is green then cross the street." In most cases, however, more complex rule sets are required, involving the integration of multiple layers of control. Although it has been shown that prefrontal cortex is important for rule representation,...