F
Fernando Díaz
Researcher at University of Valladolid
Publications - 58
Citations - 1181
Fernando Díaz is an academic researcher from University of Valladolid. The author has contributed to research in topics: Case-based reasoning & Cluster analysis. The author has an hindex of 18, co-authored 58 publications receiving 1115 citations. Previous affiliations of Fernando Díaz include University of Vigo.
Papers
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Journal ArticleDOI
Applying lazy learning algorithms to tackle concept drift in spam filtering
Florentino Fdez-Riverola,Eva Lorenzo Iglesias,Fernando Díaz,José Ramon Méndez,Juan M. Corchado +4 more
TL;DR: The results obtained are very promising and back up the idea that instance-based reasoning systems can offer a number of advantages tackling concept drift in dynamic problems, as in the case of the anti-spam filtering domain.
Journal ArticleDOI
SpamHunting: An instance-based reasoning system for spam labelling and filtering
Florentino Fdez-Riverola,Eva Lorenzo Iglesias,Fernando Díaz,José Ramon Méndez,Juan M. Corchado +4 more
TL;DR: An instance-based reasoning e-mail filtering model that outperforms classical machine learning techniques and other successful lazy learners approaches in the domain of anti-spam filtering is shown.
Journal ArticleDOI
gene‐CBR: A CASE‐BASED REASONIG TOOL FOR CANCER DIAGNOSIS USING MICROARRAY DATA SETS
TL;DR: This paper presents gene‐CBR, a hybrid model that can perform cancer classification based on microarray data that employs a case‐based reasoning model that incorporates a set of fuzzy prototypes, a growing cell structure network and aSet of rules to provide an accurate diagnosis.
Book ChapterDOI
A comparative performance study of feature selection methods for the anti-spam filtering domain
José Ramon Méndez,Florentino Fdez-Riverola,Fernando Díaz,Eva Lorenzo Iglesias,Juan M. Corchado +4 more
TL;DR: The underlying ideas behind feature selection methods are identified and applied for improving the feature selection process of SpamHunting, a novel anti-spam filtering software able to accurate classify suspicious e-mails.
Journal ArticleDOI
Reducing the Memory Size of a Fuzzy Case-Based Reasoning System Applying Rough Set Techniques
TL;DR: Experiments show that the rough sets reduction method maintains the accuracy of the employed fuzzy rules, while reducing the computational effort needed in its generation and increasing the explanatory strength of the fuzzy rules.