In 1965 Lofti A. Zadeh proposed fuzzy sets as a generalization of crisp (or classic) sets to address the incapability of crisp sets to model uncertainty and vagueness inherent in the real world. Initially, fuzzy sets did not receive a very warm welcome as many academics stood skeptical towards a theory of “imprecise” mathematics. In the middle to late 1980’s the success of fuzzy controllers bro...

Incremental reduced error pruning is a technique that has been extensively used for efficient induction of ordered rule sets (decision lists). Several criteria have been developed regarding how to prune rules and whether or not to exclude generated rules. A version of incremental reduced error pruning for unordered rule sets is presented, and the appropriateness of previously proposed criteria ...

In this paper a concern about the accuracy (as a function of parallelism) of a certain class of distributed learning algorithms is raised, and one proposed improvement is illustrated. We focus on learning a single model from a set of disjoint data sets, which are distributed across a set of computers. The model is a set of rules. The distributed data sets may be disjoint for any of several reas...

This paper describes an application of genetic algorithms (GA’s) to classify epidemiological data, which is often challenging to classify due to noise and other factors. For such complex data (that requires a large number of very specific rules to achieve a high accuracy), smaller rule sets, composed of more general rules, may be preferable, even if they are less accurate. The GA presented here...

In fuzzy rule-based models acquired from numerical data, redundancy may be present in the form of similar fuzzy sets that represent compatible concepts. This results in an unnecessarily complex and less transparent linguistic description of the system. By using a measure of similarity, a rule base simplification method is proposed that reduces the number of fuzzy sets in the model. Similar fuzz...

In data mining, Association rule mining is one of the popular and simple method to find the frequent item sets from a large dataset. While generating frequent item sets from a large dataset using association rule mining, computer takes too much time. This can be improved by using artificial bee colony algorithm (ABC). The Artificial bee colony algorithm is an optimization algorithm based on the...

In data mining, Association rule mining is one of the popular and simple method to find the frequent item sets from a large dataset. While generating frequent item sets from a large dataset using association rule mining, computer takes too much time. This can be improved by using particle swarm optimization algorithm (PSO). PSO algorithm is population based heuristic search technique used for s...

We examine the classification performance of fuzzy rule-based systems designed by three-objective genetic rule selection. While a single rule set is usually obtained from a single run of rule generation methods, multiple rule sets are simultaneously obtained by a single run of our rule selection method with three objectives: to maximize the number of correctly classified training patterns, to m...

While recent research on rule learning has focussed largely on finding highly accurate hypotheses, we evaluate the degree to which these hypotheses are also simple, that is small. To realize this, we compare well-known rule learners, such as CN2, RIPPER, PART, FOIL and C5.0 rules, with the benchmark system SL2 that explicitly aims at computing small rule sets with few literals. The results show...

Association rule mining algorithms such as Apriori were originally developed to automatically detect patterns in sales transactions and were later on also successfully applied to build collaborative filtering recommender systems (RS). Such rule mining-based RS not only share the advantages of other model-based systems such as scalability or robustness against different attack models, but also h...