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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 ...
Reducing gaps in quantitative association rules: A genetic programming free-parameter algorithm
(IOS Press, 2014)
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 ...
Improving the understanding of cancer in a descriptive way: An emerging pattern mining-based approach
(Wiley, 2021)
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 ...
Course Recommendation based on Sequences: An Evolutionary Search of Emerging Sequential Patterns
(Springer, 2022)
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. ...
An Automatic Programming ACO-Based Algorithm for Classification Rule Mining
(2017-01-20)
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 ...
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, ...
Subgroup Discovery in MOOCs: A Big Data Application for Describing Different Types of Learners
(Taylor & Francis, 2019)
The aim of this paper is to categorize and describe di erent types of learners in mas-
sive open online courses (MOOCs) by means of a subgroup discovery approach based
on MapReduce. The nal objective is to discover ...