Various techniques have been proposed for rule discovery using classification learning. In general, the learning speed of a system using genetic programming (GP) [1] is slow. However, a learning system which can acquire higher-order knowledge by adjusting to the environment can be constructed, because the structure is treated at the same time. On the other hand, there is the Apriori algorithm [...

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...

This paper describes an extension of a GAbased, separate-and-conquer propositional rule induction algorithm called SIA [24]. While the original algorithm is computationally attractive and is also able to handle both nominal and continuous attributes efficiently, our algorithm further improves it by taking into account of the recent advances in the rule induction and evolutionary computation com...

This paper describes a fuzzy rule learning system called SLAVE (Structural Learning Algorithm in Vague Environment) which learns a set of fuzzy rules from a set of examples. SLAVE has been developed for working with noise-aaected systems where the application of some conditions of classical learning theory do not produce good descriptions. This learning system allows the structure of the rule t...

in this paper, tender problems in an automobile company for procuring needed items from potential suppliers have been resolved by the learning algorithm q. in this case the purchaser with respect to proposals received from potential providers, including price and delivery time is proposed; order the needed parts to suppliers assigns. the buyer’s objective is minimizing the procurement costs thr...

Decision rules are one of the most expressive languages for machine learning. In this paper we present Adaptive Model Rules (AMRules), the first streaming rule learning algorithm for regression problems. In AMRules the antecedent of a rule is a conjunction of conditions on the attribute values, and the consequent is a linear combination of attribute values. Each rule in AMRules uses a PageHinkl...

Machine learning has been studied intensively during the past two decades. One motivation has been the desire to automate the process of knowledge acquisition during the construction of expert systems. The recent emergence of data mining as a major application for machine learning algorithms has led to the need for algorithms that can handle very large data sets. In real data mining application...

This paper presents an ASOCS (Adaptive Self-Organizing Concurrent System) model for massively parallel processing of incrementally defined rule systems in such areas as adaptive logic, robotics, logical inference, and dynamic control. An ASOCS is an adaptive network composed of many simple computing elements operating asynchronously and in parallel. This paper focuses on Adaptive Algorithm 2 (A...

Most machine translators are implemented using example based, rule based, and statistical approaches. However, each of these paradigms has its drawbacks. Example based and statistical based approaches are domain specific and requires a large database of examples to produce accurate translation results. Although rule based approach is known to produce high quality translations, a linguist is nec...