This paper presents a new boosting algorithm for genetic learning of fuzzy classification rules. The method is based on the iterative rule learning approach to fuzzy rule base system design. The fuzzy rule base is built in an incremental fashion, in that the evolutionary algorithm extracts one fuzzy classifier rule at a time. The boosting mechanism reduces the weight of those training instances...

We present IREP++, a rule learning algorithm similar to RIPPER and IREP. Like these other algorithms IREP++ produces accurate, human readable rules from noisy data sets. However IREP++ is able to produce such rule sets more quickly and can often express the target concept with fewer rules and fewer literals per rule resulting in a concept description that is easier for humans to understand. The...

It is one of the key problems for web based decision support systems to generate knowledge from huge database containing inconsistent information. In this paper, a learning algorithm for multiple rule trees (MRT) is developed, which is based on ID3 algorithm and rough set theory. MRT algorithm can quickly generate decision rules from inconsistent decision information tables. Both space and time...

The proposed IAFC neural networks have both stability and plasticity because theyuse a control structure similar to that of the ART-1(Adaptive Resonance Theory) neural network.The unsupervised IAFC neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. This fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. The supervised IAFC ...

In this paper, an algorithm for datadriven incremental learning of fuzzy basis function networks is presented. A modified version of vector quantization is exploited for rule evolution and incremental learning of the rules’ antecedent parts. Antecedent learning is connected in a stable manner with a recursive learning of rule consequent functions with linear parameters. The paper is concluded w...

The paper describes an inductive learning environment called DELVAUX for classiication tasks that learns PROSPECTOR-style, Bayesian classiication 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 oospring through the exchange of rules, permitting tter rule-sets to produce oospring ...

In fields like medical care, the temporal relations in the records (transactions) are of great help for identifying a particular group of cases. Thus there is some need for sequence rule learning in the classification problems in these fields. In this paper, a genetic algorithm for sequence rule learning is presented based on concepts from learning behavior of agents. The algorithm employs a Mi...

This paper presents a novel boosting algorithm for genetic learning of fuzzy classification rules. The method is based on the iterative rule learning approach to fuzzy rule base system design. The fuzzy rule base is generated in an incremental fashion, in that the evolutionary algorithm optimizes one fuzzy classifier rule at a time. The boosting mechanism reduces the weight of those training in...

This paper describes RULES-4, a new algorithm for incremental inductive learning from the “RULES” family of automatic rule extraction systems. This algorithm is the first incremental learning system in the family. It has a number of advantages over well known non-incremental schemes. It allows the stored knowledge to be updated and refined rapidly when new examples are available. The induction ...

In this paper, a new computational model of associative learning is proposed, which is based on the Ising model. Application of the stochastic gradient descent algorithm to the proposed model yields an on-line learning rule. Next, it is shown that the obtained new learning rule generalizes two well-known learning rules, i.e., the Hebbian rule and the Oja's rule. Later, the fashion of incorporat...