Displaying similar documents to “Learning the naive Bayes classifier with optimization models”

Rough modeling - a bottom-up approach to model construction

Terje Loken, Jan Komorowski (2001)

International Journal of Applied Mathematics and Computer Science

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Traditional data mining methods based on rough set theory focus on extracting models which are good at classifying unseen obj-ects. If one wants to uncover new knowledge from the data, the model must have a high descriptive quality-it must describe the data set in a clear and concise manner, without sacrificing classification performance. Rough modeling, introduced by Kowalczyk (1998), is an approach which aims at providing models with good predictive emphand descriptive qualities, in...

Bayesian methods in hydrology: a review.

David Ríos Insua, Raquel Montes Díez, Jesús Palomo Martínez (2002)

RACSAM

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Hydrology and water resources management are inherently affected by uncertainty in many of their involved processes, including inflows, rainfall, water demand, evaporation, etc. Statistics plays, therefore, an essential role in their study. We review here some recent advances within Bayesian statistics and decision analysis which will have a profound impact in these fields.

Evolutionary computation based on Bayesian classifiers

Teresa Miquélez, Endika Bengoetxea, Pedro Larrañaga (2004)

International Journal of Applied Mathematics and Computer Science

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Evolutionary computation is a discipline that has been emerging for at least 40 or 50 years. All methods within this discipline are characterized by maintaining a set of possible solutions (individuals) to make them successively evolve to fitter solutions generation after generation. Examples of evolutionary computation paradigms are the broadly known Genetic Algorithms (GAs) and Estimation of Distribution Algorithms (EDAs). This paper contributes to the further development of this discipline...

A model for credit scoring: an application of discriminant analysis.

Manuel Artís, Montserrat Guillén, José M.ª Martínez (1994)

Qüestiió

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The application of statistical techniques in decision making, and more specifically for classification requirements, has proved to be adequate in the context of financial problems. In this study, we present the methodology used and the results obtained in the elaboration of a decision-support system for credit assignment. The problem was to provide an automatic tool for a Spanish financial institution that needed to quantify and analyse credit applications from clients. Firstly, we shall...

Text document classification based on mixture models

Jana Novovičová, Antonín Malík (2004)

Kybernetika

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Finite mixture modelling of class-conditional distributions is a standard method in a statistical pattern recognition. This paper, using bag-of-words vector document representation, explores the use of the mixture of multinomial distributions as a model for class-conditional distribution for multiclass text document classification task. Experimental comparison of the proposed model and the standard Bernoulli and multinomial models as well as the model based on mixture of multivariate...

Conceptual base of feature selection consulting system

Pavel Pudil, Jana Novovičová, Petr Somol, Radek Vrňata (1998)

Kybernetika

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The paper briefly reviews recent advances in the methodology of feature selection (FS) and the conceptual base of a consulting system for solving FS problems. The reasons for designing a kind of expert or consulting system which would guide a less experienced user are outlined. The paper also attempts to provide a guideline which approach to choose with respect to the extent of a priori knowledge of the problem. The methods discussed here form the core of the software package being developed...