1,721,066 research outputs found
Improved SOM Labeling Methodology for Data Mining Applications, In Soft Computing for Knowledge Discovery and Data Mining (O. Maimon and L. Rokach, Eds.)"
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A note on knowledge discovery using neural networks and its application to credit card screening
No full textWe address an important issue in knowledge discovery using neural networks that has been left out in a recent article “Knowledge discovery using a neural network simultaneous optimization algorithm on a real world classification problem” by Sexton et al. [R.S. Sexton, S. McMurtrey, D.J. Cleavenger, Knowledge discovery using a neural network simultaneous optimization algorithm on a real world classification problem, European Journal of Operational Research 168 (2006) 1009–1018]. This important issue is the generation of comprehensible rule sets from trained neural networks. In this note, we present our neural network rule extraction algorithm that is very effective in discovering knowledge embedded in a neural network. This algorithm is particularly appropriate in applications where comprehensibility as well as accuracy are required. For the same data sets used by Sexton et al. our algorithm produces accurate rule sets that are concise and comprehensible, and hence helps validate the claim that neural networks could be viable alternatives to other data mining tools for knowledge discovery
Recursive neural network rule extraction for data with mixed attributes
No full textIn this paper, we present a recursive algorithm for extracting classification rules from feedforward neural networks (NNs) that have been trained on data sets having both discrete and continuous attributes. The novelty of this algorithm lies in the conditions of the extracted rules: the rule conditions involving discrete attributes are disjoint from those involving continuous attributes. The algorithm starts by first generating rules with discrete attributes only to explain the classification process of the NN. If the accuracy of a rule with only discrete attributes is not satisfactory, the algorithm refines this rule by recursively generating more rules with discrete attributes not already present in the rule condition, or by generating a hyperplane involving only the continuous attributes. We show that for three real-life credit scoring data sets, the algorithm generates rules that are not only more accurate but also more comprehensible than those generated by other NN rule extraction methods.<br/
Risk Management and Regulatory Compliance: a Data Mining Framework Based on Neural Network Rule Extraction
Full text linkICIS 2006 Proceedings - Twenty Seventh International Conference on Information Systems71-8
Building intelligent credit-risk evaluation systems using neural network rule extraction and decision tables
No full textTable of contentsNeo-classical reengineering: Returning to the promise of process in the post-Internet economyM. De Kegel and M. McDonaldTowards an integrative framework for software architectureR. Maes and G. DedeneComponent based development. From dinosaurs to small, adaptive, co-operating, replaceable creaturesG. Van Humbeeck, J. MerckxSeparating Business Process Aspects from Business Object behaviourM. SnoeckCOSMIC-FFP and MERODE: Applying the Next Generation Function Points to Object Oriented Enterprise ModelsG. PoelsOn the use of Jackson Structured Programming (JSP) for the structured design of XSL TransformationsG. DedeneRuling the business: about Business Rules, decision tables and Intelligent AgentsJ. VanthienenBuilding intelligent credit-risk evaluation systems using neural network rule extraction and decision tablesB. Baesens, R. Setiono, C. Mues, S. Viaene and J. VanthienenWeb service description, advertising and discovery: WSDL and beyondW. LemahieuDeveloping enterprise architecture: the case of KBC InsuranceF. Pieck, S. Viaene and G. Deden
Rule extraction from minimal neural networks for credit card screening
No full text: While feedforward neural networks have been widely accepted as effective tools for solving classification problems, the issue of finding the best network architecture remains unresolved, particularly so in real-world problem settings. We address this issue in the context of credit card screening, where it is important to not only find a neural network with good predictive performance but also one that facilitates a clear explanation of how it produces its predictions. We show that minimal neural networks with as few as one hidden unit provide good predictive accuracy, while having the added advantage of making it easier to generate concise and comprehensible classification rules for the user. To further reduce model size, a novel approach is suggested in which network connections from the input units to this hidden unit are removed by a very straightaway pruning procedure. In terms of predictive accuracy, both the minimized neural networks and the rule sets generated from them are shown to compare favorably with other neural network based classifiers. The rules generated from the minimized neural networks are concise and thus easier to validate in a real-life setting
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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