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 ...

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 ...

The extraction of useful information for decision making is a challenge in many different domains. Association rule mining is one of the most important techniques in this field, discovering relationships of interest among ...

Skin cancer is one of the most common types of cancers in the world, with melanoma being the most lethal form. Automatic melanoma diagnosis from skin images has recently gained attention within the machine learning community, ...

Multi-label classification has been used to solve a wide range of problems where each example in the dataset may be related either to one class (as in traditional classification problems) or to several class labels at the ...

This paper presents an approach based on emerging pattern mining to analyse cancer through genomic data. Unlike existing approaches, mainly focused on predictive purposes, the proposal aims to improve the understanding of ...

To provide a good study plan is key to avoid students’ failure. Academic advising based on student’s preferences, complexity of the semester, or even background knowledge is usually considered to reduce the dropout rate. ...

Este artículo muestra la utilización de los algoritmos evolutivos para el descubrimiento de reglas de predicción que se utilizarán en la mejora de Cursos Hipermedia Adaptativos basados en Web. Se ha desarrollado una ...

In this paper we present a novel algorithm, named GBAP, that jointly uses automatic programming with ant colony optimization for mining classification rules. GBAP is based on a context-free grammar that properly guides ...