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Autómatas celulares y aplicaciones
(FISEM, 2016)
Un autómata celular es un modelo matemático para un sistema dinámico
que evoluciona en pasos discretos. Este trabajo presenta una aplicación
de los autómatas celulares para el cifrado de información y el reparto de
secretos. ...
LAIM discretization for multi-label data
(2017)
Multi-label learning is a challenging task in data mining which has attracted growing attention in recent years. Despite the fact that many multi-label datasets have continuous features, general algorithms developed specially ...
Evaluación distribuida transparente para algoritmos evolutivos en JCLEC
(2017)
La evaluaci ´on de los individuos en un algoritmo evolutivo constituye
generalmente la etapa con un mayor coste computacional. Este hecho se acent ´ua
en los problemas de miner´ıa de datos debido al cada vez mayor tama˜no ...
Multi-Objective Genetic Programming for Feature Extraction and Data Visualization
(2017)
Feature extraction transforms high dimensional
data into a new subspace of lower dimensionalitywhile keeping
the classification accuracy. Traditional algorithms do not
consider the multi-objective nature of this task. ...
Classification Rule Mining with Iterated Greedy
(2017-03-30)
In the context of data mining, classi cation rule discovering
is the task of designing accurate rule based systems that model the useful
knowledge that di erentiate some data classes from others, and is present
in large ...
JCLEC Meets WEKA!
(2014-02-27)
WEKA has recently become a very referenced DM tool. In
spite of all the functionality it provides, it does not include any framework
for the development of evolutionary algorithms. An evolutionary
computation framework ...
A Classification Module for Genetic Programming Algorithms in JCLEC
(MIT Press, 2014)
JCLEC-Classi cation is a usable and extensible open source library for genetic program-
ming classi cation algorithms. It houses implementations of rule-based methods for clas-
si cation based on genetic programming, ...
ur-CAIM: Improved CAIM Discretization for Unbalanced and Balanced Data
(2015-10-15)
Supervised discretization is one of basic data preprocessing
techniques used in data mining. CAIM (Class-
Attribute InterdependenceMaximization) is a discretization
algorithm of data for which the classes are known. ...
Parallel evaluation of Pittsburgh rule-based classifiers on GPUs
(2017-01-19)
Individuals from Pittsburgh rule-based classifiers represent a complete solution
to the classification problem and each individual is a variable-length set
of rules. Therefore, these systems usually demand a high level ...
Speeding up Multiple Instance Learning Classification Rules on GPUs
(2017)
Multiple instance learning is a challenging task in supervised learning and data mining. How-
ever, algorithm performance becomes slow when learning from large-scale and high-dimensional data sets.
Graphics processing ...