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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1...112 113 114 115 116 Hits 2.301 – 2.304 of 2.304 2301 A compact evolutionary interval-valued fuzzy rule-based classification system for the modeling and prediction of real-world financial applications with imbalanced data Sanz Delgado, José Antonio; Bernardo, Darío; Herrera, Francisco. - : IEEE BASE Show details 2302 CFM-BD: a distributed rule induction algorithm for building compact fuzzy models in Big Data classification problems Elkano Ilintxeta, Mikel; Sanz Delgado, José Antonio; Barrenechea Tartas, Edurne. - : IEEE BASE Show details 2303 Improving the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets and genetic amplitude tuning Sanz Delgado, José Antonio; Fernández, Alberto; Bustince Sola, Humberto; Herrera, Francisco. - : Elsevier Abstract: Among the computational intelligence techniques employed to solve classification problems, Fuzzy Rule-Based Classification Systems (FRBCSs) are a popular tool because of their interpretable models based on linguistic variables, which are easier to understand for the experts or end-users. The aim of this paper is to enhance the performance of FRBCSs by extending the Knowledge Base with the application of the concept of Interval-Valued Fuzzy Sets (IVFSs). We consider a post-processing genetic tuning step that adjusts the amplitude of the upper bound of the IVFS to contextualize the fuzzy partitions and to obtain a most accurate solution to the problem. We analyze the goodness of this approach using two basic and well-known fuzzy rule learning algorithms, the Chi et al.’s method and the fuzzy hybrid genetics-based machine learning algorithm. We show the improvement achieved by this model through an extensive empirical study with a large collection of data-sets. ; This work has been supported by the Spanish Ministry of Science and Technology under projects TIN2008-06681-C06-01 and TIN2007-65981. Keyword: Fuzzy rule-based classification systems; Genetic algorithms; Interval-valued fuzzy sets; Tuning URL: https://hdl.handle.net/2454/17683 https://doi.org/10.1016/j.ins.2010.06.018 BASE Hide details 2304 A genetic tuning to improve the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets: degree of ignorance and lateral position Sanz Delgado, José Antonio; Fernández, Alberto; Bustince Sola, Humberto. - : Elsevier BASE Show details Page: 1...112 113 114 115 116

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