Rule Learning Method Research Articles

NEW CRITERIA FOR RULE SELECTION IN FUZZY LEARNING CLASSIFIER SYSTEMS

Designing an effective criterion for selecting the best rule is a major problem in the process of implementing Fuzzy Learning Classifier (FLC) systems. Conventionally confidence and support or combined measures of these are used as criteria for fuzzy rule evaluation. In this paper new entities namely precision and recall from the field of Information Retrieval (IR) systems is adapted as alternative criteria for fuzzy rule evaluation. Several different combinations of precision and recall are redesigned to produce a metric measure. These newly introduced criteria are utilized as a rule selection mechanism in the method of Iterative Rule Learning (IRL) of FLC. In several experiments, three standard datasets are used to compare and contrast the novel IR based criteria with other previously developed measures. Experimental results illustrate the effectiveness of the proposed techniques in terms of classification performance and computational efficiency.

Fuzzy Classification Rule Learning by Decision Tree Induction

Knowledge acquisition is a bottleneck in knowledge-based system implementation. Decision tree induction is a useful machine learning approach for extracting classification knowledge from a set of training examples. Many real-world data contain fuzziness due to observation error, uncertainty, subjective judgement, and so on. To cope with this problem of real-world data, there have been some works on fuzzy classification rule learning. This paper makes a survey for the kinds of fuzzy classification rules. In addition, it presents a fuzzy classification rule learning method based on decision tree induction, and shows some experiment results for the method.

COR: a methodology to improve ad hoc data-driven linguistic rule learning methods by inducing cooperation among rules

This paper introduces a new learning methodology to quickly generate accurate and simple linguistic fuzzy models: the cooperative rules (COR) methodology. It acts on the consequents of the fuzzy rules to find those that are best cooperating. Instead of selecting the consequent with the highest performance in each fuzzy input subspace, as ad-hoc data-driven methods usually do, the COR methodology considers the possibility of using another consequent, different from the best one, when it allows the fuzzy model to be more accurate thanks to having a rule set with the best cooperation. Our proposal has shown good results in solving three different applications when compared to other methods.

Fuzzy modeling by hierarchically built fuzzy rule bases

Although Mamdani-type fuzzy rule-based systems (FRBSs) became successfully performing clearly interpretable fuzzy models, they still have some lacks related to their accuracy when solving complex problems. A variant of these kinds of systems, which allows to perform a more accurate model representation, are the so-called approximate FRBSs. This alternative representation still cannot avoid the problems concerning the fuzzy rule learning methods, which as prototype identification algorithms, try to extract those approximate rules from the object problem space. In this paper we deal with the previous problems, viewing fuzzy models as a class of local modeling approaches which attempt to solve a complex problem by decomposing it into a number of simpler subproblems with smooth transitions between them. In order to develop this class of models, we first propose a common framework to characterize available approximate fuzzy rule learning methods, and later we modify it by introducing a fuzzy rule base hierarchical learning methodology (FRB-HLM). This methodology is based on the extension of the simple building process of the fuzzy rule base of FRBSs in a hierarchical way, in order to make the system more accurate. This flexibilization will allow us to have fuzzy rules with different degrees of specificity, and thus to improve the modeling of those problem subspaces where the former models have bad performance, as a refinement. This approach allows us not to have to assume a fixed number of rules and to integrate the good local behavior of the hierarchical model with the global model, ensuring a good global performance.

Design of a radial basis function neural network with a radius-modification algorithm using response surface methodology

A radial basis function (RBF) neural network was designed for time series forecasting using both an adaptive learning algorithm and response surface methodology (RSM). To improve the traditional RBF network‘s forecasting capability, the generalized delta rule learning method was employed to modify the radius of the kernel function. Then RSM was utilized to explore the mean square error response surface so that the appropriate combination of network parameters, such as the number of hidden nodes and the initial learning rates, could be found. Extensive studies were performed on the effect of the initial values of connection weights on the accuracy of the backpropagation learning method that was employed in the training of the RBF artificial neural network. The effectiveness of the neural network with the proposed radius-modification technique and the RSM method was demonstrated with an example of forecasting intensity pulsations of a laser. It was found that, by utilizing the proposed techniques, the neural network provided a more accurate prediction of the response.

An efficient inductive learning method for object-oriented database using attribute entropy

The data-driven characteristic of the Version Space rule-learning method works efficiently in memory even if the training set is enormous. However, the concept hierarchy of each attribute used to generalize/specialize the hypothesis of a specific/general (S/G) set is processed sequentially and instance by instance, which degrades its performance. As for ID3, the decision tree is generated from the order of attributes according to their entropies to reduce the number of attributes in some of the tree paths. Unlike Version Space, ID3 generates an extremely complex decision tree when the training set is enormous. Therefore, we propose a method called AGE (A_RCH+OG_L+ASE_, where ARCH=Automatic geneRation of Concept Hierarchies, OGL=Optimal Generalization Level, and ASE=Attribute Selection by Entropy), taking advantages of Version Space and ID3 to learn rules from object-oriented databases (OODBs) with the least number of learning features according to the entropy. By simulations, we found the performance of our learning algorithm is better than both Version Space and ID3. Furthermore, AGE's time complexity and space complexity are both linear with the number of training instances.

Evolving prototype control rules for a dynamic system

The genetic algorithm is used for the learning of prototype control rules for a dynamic system. Prototype control rules are point based, but only a limited number of points in the state space with associated control actions are learned. The nearest-neighbour algorithm is used to decide which of the rules to fire in any situation. The example of a simulated cart-pole balancing problem is used to demonstrate the advantages of this approach over other rule-learning methods.

法則学習における「験証」法の効果

Although it is generally accepted that human reasoning can be described as either inductive or deductive processes, this study proposes a new method of rule learning based on Popper's idea of “corroboration” as human reasoning process.“Corroboration” is defined as an application of a hypothesis (rule), which was induced from a few examples, to new examples in order to confirm the validity of the hypothesis. In two experiments, 170 undergraduates read one of three materials describing an unfamiliar mathematical rule and its examples. Each material embodied one of three processes i. e., inductive, deductive, or “corroborative” processes. In the results, those who read the “corroborative” material were better than others in three aspects:(1) They had more confidence that the rule was correct.(2) Their motivation for the application of the rule to other examples was well satisfied.(3) They could recall the rule better even a week later.