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Classification Rules (classification + rule)
Selected AbstractsIdiot's Bayes,Not So Stupid After All?INTERNATIONAL STATISTICAL REVIEW, Issue 3 2001David J. Hand Summary Folklore has it that a very simple supervised classification rule, based on the typically false assumption that the predictor variables are independent, can be highly effective, and often more effective than sophisticated rules. We examine the evidence for this, both empirical, as observed in real data applications, and theoretical, summarising explanations for why this simple rule might be effective. Résumé La tradition veunt qu'une règle très simple assumant l'independance des variables prédictives. une hypothèse fausse dans la plupart des cas, peut être très efficace, souvent même plus efficace qu'une méthode plus sophistiquée en ce qui concerne l'attribution de classes a un groupe d'objets. A ce sujet, nous examinons les preuves empiriques, et les preuves théoriques, e'est-a-dire les raisons pour lesquelles cette simple règle pourrait faciliter le processus de tri. [source] Using unlabelled data to update classification rules with applications in food authenticity studiesJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 1 2006Nema Dean Summary., An authentic food is one that is what it purports to be. Food processors and consumers need to be assured that, when they pay for a specific product or ingredient, they are receiving exactly what they pay for. Classification methods are an important tool in food authenticity studies where they are used to assign food samples of unknown type to known types. A classification method is developed where the classification rule is estimated by using both the labelled and the unlabelled data, in contrast with many classical methods which use only the labelled data for estimation. This methodology models the data as arising from a Gaussian mixture model with parsimonious covariance structure, as is done in model-based clustering. A missing data formulation of the mixture model is used and the models are fitted by using the EM and classification EM algorithms. The methods are applied to the analysis of spectra of food-stuffs recorded over the visible and near infra-red wavelength range in food authenticity studies. A comparison of the performance of model-based discriminant analysis and the method of classification proposed is given. The classification method proposed is shown to yield very good misclassification rates. The correct classification rate was observed to be as much as 15% higher than the correct classification rate for model-based discriminant analysis. [source] Comparing a genetic fuzzy and a neurofuzzy classifier for credit scoringINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 11 2002F. Hoffmann In this paper, we evaluate and contrast two types of fuzzy classifiers for credit scoring. The first classifier uses evolutionary optimization and boosting for learning fuzzy classification rules. The second classifier is a fuzzy neural network that employs a fuzzy variant of the classic backpropagation learning algorithm. The experiments are carried out on a real life credit scoring data set. It is shown that, for the case at hand, the boosted genetic fuzzy classifier performs better than both the neurofuzzy classifier and the well-known C4.5(rules) decision tree(rules) induction algorithm. However, the better performance of the genetic fuzzy classifier is offset by the fact that it infers approximate fuzzy rules which are less comprehensible for humans than the descriptive fuzzy rules inferred by the neurofuzzy classifier. © 2002 Wiley Periodicals, Inc. [source] Microarray data classification using inductive logic programming and gene ontology background informationJOURNAL OF CHEMOMETRICS, Issue 5 2010Einar Ryeng Abstract There exists many databases containing information on genes that are useful for background information in machine learning analysis of microarray data. The gene ontology and gene ontology annotation projects are among the most comprehensive of these. We demonstrate how inductive logic programming (ILP) can be used to build classification rules for microarray data which naturally incorporates the gene ontology and annotations to it as background knowledge without removing the inherent graph structure of the ontology. The ILP rules generated are parsimonious and easy to interpret. Copyright © 2010 John Wiley & Sons, Ltd. [source] Using unlabelled data to update classification rules with applications in food authenticity studiesJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 1 2006Nema Dean Summary., An authentic food is one that is what it purports to be. Food processors and consumers need to be assured that, when they pay for a specific product or ingredient, they are receiving exactly what they pay for. Classification methods are an important tool in food authenticity studies where they are used to assign food samples of unknown type to known types. A classification method is developed where the classification rule is estimated by using both the labelled and the unlabelled data, in contrast with many classical methods which use only the labelled data for estimation. This methodology models the data as arising from a Gaussian mixture model with parsimonious covariance structure, as is done in model-based clustering. A missing data formulation of the mixture model is used and the models are fitted by using the EM and classification EM algorithms. The methods are applied to the analysis of spectra of food-stuffs recorded over the visible and near infra-red wavelength range in food authenticity studies. A comparison of the performance of model-based discriminant analysis and the method of classification proposed is given. The classification method proposed is shown to yield very good misclassification rates. The correct classification rate was observed to be as much as 15% higher than the correct classification rate for model-based discriminant analysis. [source] Risk Criteria for the Shipping IndustryQUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 1 2006V. M. Trbojevic Abstract This paper proposes dual risk criteria for the shipping industry based on a goal-setting approach to safety requiring risk in the tolerability region to be as low as reasonably practicable (ALARP) within which there is a prescriptive target risk level which should not be reached. The prescriptive target risk level is intended for smaller companies satisfying the current safety and classification rules, while it is expected that leading companies would embrace the dynamic ALARP approach as a means for improving the current practice in a search for a better and more economical solution. Furthermore, it is shown that it is possible to formulate the societal risk criteria fully consistent with often legally imposed individual risk criteria. Copyright © 2006 John Wiley & Sons, Ltd. [source] | ||||||||||