Rule Extraction, Fuzzy ARTMAP, and Medical Databases

This paper shows how knowledge, in the form of fuzzy rules, can be derived from a self-organizing supervised learning neural network called fuzzy ARTMAP. Rule extraction proceeds in two stages: pruning removes those recognition nodes whose confidence index falls below a selected threshold; and quantization of continuous learned weights allows the final system state to be translated into a usable set of rules. Simulations on a medical prediction problem, the Pima Indian Diabetes (PID) database, illustrate the method. In the simulations, pruned networks about 1/3 the size of the original actually show improved performance. Quantization yields comprehensible rules with only slight degradation in test set prediction performance.British Petroleum (89-A-1204); Defense Advanced Research Projects Agency (AFOSR-90-0083, ONR-N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (90-0083); Institute of Systems Science (National University of Singapore

Fuzzy rule extraction for controller designs

[[abstract]]This paper presents an innovative method for extracting fuzzy rules directly from numerical data for controller designs. Conventional approaches to fuzzy systems assume there is no correlation among features and therefore involve dividing the input and output space into grid regions. However, in most cases, it is likely that features are highly correlated. Therefore, we propose to use an aggregation of hyperspheres with different sizes and different positions to define fuzzy rules. The genetic algorithm is used to select the parameters of the proposed fuzzy systems. The inverted pendulum system is utilized to illustrate the efficiency of the proposed method for finding fuzzy control rules.[[conferencetype]]國際[[conferencedate]]19950522~19950527[[iscallforpapers]]Y[[conferencelocation]]Taipei, Taiwa

Process identification through modular neural networks and rule extraction

Monolithic neural networks may be trained from measured data to establish knowledge about the process. Unfortunately, this knowledge is not guaranteed to be found and - if at all - hard to extract. Modular neural networks are better suited for this purpose. Domain-ordered by topology, rule extraction is performed module by module. This has all the benefits of a divide-and-conquer method and opens the way to structured design. This paper discusses a next step in this direction by illustrating the potential of base functions to design the neural model. \ud [Full paper published as: Berend Jan van der Zwaag, Kees Slump, and Lambert Spaanenburg. Process identification through modular neural networks and rule extraction. In Proceedings FLINS-2002, Ghent, Belgium, 16-18 Sept. 2002.

Rule Extraction by Genetic Programming with Clustered Terminal Symbols

When Genetic Programming (GP) is applied to rule extraction from databases, the attributes of the data are often used for the terminal symbols. However, in the case of the database with a large number of attributes, the search space becomes vast because the size of the terminal set increases. As a result, the search performance declines. For improving the search performance, we propose new methods for dealing with the large-scale terminal set. In the methods, the terminal symbols are clustered based on the similarities of the attributes. In the beginning of search, by reducing the number of terminal symbols, the rough and rapid search is performed. In the latter stage of search, by using the original attributes for terminal symbols, the local search is performed. By comparison with the conventional GP, the proposed methods showed the faster evolutional speed and extracted more accurate classification rules

Hybrid rule-extraction from support vector machines

Rule-extraction from artificial neural networks(ANNs) as well as support vector machines (SVMs) provide explanations for the decisions made by these systems. This explanation capability is very important in applications such as medical diagnosis. Over the last decade, a multitude of algorithms for rule-extraction from ANNs have been developed. However, rule-extraction from SVMs is not widely available yet.In this paper, a hybrid approach for rule-extraction from SVMs is outlined. This approach has two basic components: (1) data reduction using a logistic regression model and (2) learning based rule-extraction. The quality of the extracted rules is then evaluated in terms of fidelity, accuracy, consistency and comprehensibility. The rules are also verified against the available knowledge from the domain problem (diabetes) to assure correctness and validity

EXTRACTING RULES FROM TRAINED RBF NEURAL NETWORKS

This paper describes a method of rule extraction from trained artificial neural networks. The statement of the problem is given. The aim of rule extraction procedure and suitable neural networks for rule extraction are outlined. The RULEX rule extraction algorithm is discussed that is based on the radial basis function (RBF) neural network. The extracted rules can help discover and analyze the rule set hidden in data sets. The paper contains an implementation example, which is shown through standalone IRIS data set

Analysis of Modified Rule Extraction Algorithm and Internal Representation of Neural Network

Classification and Rule extraction is an important application of Artificial Neural Network. To extract fewer rules from multilayer feed forward neural network has been a research area. The internal representation of the network is augmented by a distance term to extract fewer rules from the feedforward neural network and experimented on five datasets. Understanding affect of different factors of the dataset and network on extraction of a number of rules from the network can reveal important pieces of information which may help researchers to enhance the rule extraction process. This work investigates the internal behavior of neural network in rule extraction process on five different dataset.Keywords: Rule extraction, Feed Forward Neural Network, Hidden units, Activation value, Hidden neurons