When upper probabilities are possibility measures
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A practical approach to feature selection
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Genetic algorithms + data structures = evolution programs (3rd ed.)
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A random sets-based method for identifying fuzzy models
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Construction of fuzzy knowledge bases incorporating feature selection
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Higher order models for fuzzy random variables
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Obtaining transparent models of chaotic systems with multi-objective simulated annealing algorithms
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Input features' impact on fuzzy decision processes
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Induction of fuzzy-rule-based classifiers with evolutionary boosting algorithms
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Fuzzy-Rough Sets Assisted Attribute Selection
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Advocating the Use of Imprecisely Observed Data in Genetic Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Diagnosis of dyslexia with low quality data with genetic fuzzy systems
International Journal of Approximate Reasoning
On dynamic soft dimension reduction in evolving fuzzy classifiers
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
On-line incremental feature weighting in evolving fuzzy classifiers
Fuzzy Sets and Systems
Upper and lower probabilities induced by a fuzzy random variable
Fuzzy Sets and Systems
Upper and lower probabilities induced by a fuzzy random variable
Fuzzy Sets and Systems
Mark-recapture techniques in statistical tests for imprecise data
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
An study of the tree generation algorithms in equation based model learning with low quality data
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Review: A framework for awareness maintenance
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Analysing the low quality of the data in lighting control systems
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Comparison of fuzzy functions for low quality data GAP algorithms
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Inner and outer fuzzy approximations of confidence intervals
Fuzzy Sets and Systems
Feature subset selection Filter-Wrapper based on low quality data
Expert Systems with Applications: An International Journal
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Algorithms for preprocessing databases with incomplete and imprecise data are seldom studied. For the most part, we lack numerical tools to quantify the mutual information between fuzzy random variables. Therefore, these algorithms (discretization, instance selection, feature selection, etc.) have to use crisp estimations of the interdependency between continuous variables, whose application to vague datasets is arguable. In particular, when we select features for being used in fuzzy rule-based classifiers, we often use a mutual information-based ranking of the relevance of inputs. But, either with crisp or fuzzy data, fuzzy rule-based systems route the input through a fuzzification interface. The fuzzification process may alter this ranking, as the partition of the input data does not need to be optimal. In our opinion, to discover the most important variables for a fuzzy rule-based system, we want to compute the mutual information between the fuzzified variables, and we should not assume that the ranking between the crisp variables is the best one. In this paper we address these problems, and propose an extended definition of the mutual information between two fuzzified continuous variables. We also introduce a numerical algorithm for estimating the mutual information from a sample of vague data. We will show that this estimation can be included in a feature selection algorithm, and also that, in combination with a genetic optimization, the same definition can be used to obtain the most informative fuzzy partition for the data. Both applications will be exemplified with the help of some benchmark problems.