On choice rules in dependent type theory

In a dependent type theory satisfying the propositions as types correspondence together with the proofs-as-programs paradigm, the validity of the unique choice rule or even more of the choice rule says that the extraction of a computable witness from an existential statement under hypothesis can be performed within the same theory. Here we show that the unique choice rule, and hence the choice rule, are not valid both in Coquand\u2019s Calculus of Constructions with indexed sum types, list types and binary disjoint sums and in its predicative version implemented in the intensional level of the Minimalist Founda- tion. This means that in these theories the extraction of computational witnesses from existential statements must be performed in a more ex- pressive proofs-as-programs theory

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

Building Credit-Risk Evaluation Expert Systems Using Neural Network Rule Extraction and Decision Tables.

In this paper, we evaluate and contrast four neural network rule extraction approaches for credit scoring. Experiments are carried out on three real life credit scoring data sets. Both the continuous and the discretised versions of all data sets are analysed. The rule extraction algorithms, Neurolinear, Neurorule, Trepan and Nefclass, have different characteristics with respect to their perception of the neural network and their way of representing the generated rules or knowledge. It is shown that Neurolinear, Neurorule and Trepan are able to extract very concise rule sets or trees with a high predictive accuracy when compared to classical decision tree (rule) induction algorithms like C4.5(rules). Especially Neurorule extracted easy to understand and powerful propositional ifthen rules for all discretised data sets. Hence, the Neurorule algorithm may offer a viable alternative for rule generation and knowledge discovery in the domain of credit scoring.Credit; Information systems; International; Systems;

Network Neutrality and the False Promise of Zero-Price Regulation

This Article examines zero-price regulation, the major distinguishing feature of many modern "network neutrality" proposals. A zero-price rule prohibits a broadband Internet access provider from charging an application or content provider (collectively, "content provider") to send information to consumers. The Article differentiates two access provider strategies thought to justify a zero-price rule. Exclusion is anticompetitive behavior that harms a content provider to favor its rival. Extraction is a toll imposed upon content providers to raise revenue. Neither strategy raises policy concerns that justify implementation of a broad zero-price rule. First, there is no economic exclusion argument that justifies the zero-price rule as a general matter, given existing legal protections against exclusion. A stronger but narrow argument for regulation exists in certain cases in which the output of social producers, such as Wikipedia, competes with ordinary market-produced content. Second, prohibiting direct extraction is undesirable and counterproductive, in part because it induces costly and unregulated indirect extraction. I conclude, therefore, that recent calls for broad-based zero-price regulation are mistaken.

Sustainable growth: The extraction-saving relationship

The paper presents two new results for the Dasgupta-Heal-Solow-Stiglitz model with an essential nonrenewable resource: (1) the pattern of resource extraction can be more important for sustainable growth than the pattern of saving when the Hotelling Rule modifier is not small enough; (2) the qualitative behavior of the long-run per capita consumption can be examined along any smooth enough path of extraction using the "sustainability functional" introduced in the paper.sustainable growth; modified Hotelling Rule; sustainability number; Hubbert curve consumption

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.

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

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