Paper Info
Title
A meta-learning based framework for building algorithm recommenders: An application for educational arena.
Abstract
The task of selecting the most suitable classification algorithm for each data set under analysis is still today a unsolved research problem. This paper therefore proposes a meta-learning based framework that helps both, practitioners and non-experts data mining users to make informed decisions about the goodness and suitability of each available technique for their data set at hand. In short, the framework is supported by an experimental database that is fed with the meta-features extracted from training data sets and the performance obtained by a set of classifiers applied over them, with the aim of building an algorithm recommender using regressors. This will allow the end-user to know, for a new unseen data set, the predicted accuracy of this set of algorithms ranked by this value. The experimentation performed and discussed in this paper is addressed to evaluate which meta-features are more significant and useful for characterising data sets with the end goal of building algorithm recommenders and to test the feasibility of these recommenders. The study is carried out on data sets from the educational arena, in particular, targeted to predict students' performance in e-learning courses.
Year
DOI
Venue
2017
10.3233/JIFS-169141
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
Keywords
Field
DocType
Meta-learning,regression,student performance,educational data mining
Data science,Artificial intelligence,Mathematics,Machine learning
Journal
Volume
Issue
ISSN
32
2
1064-1246
Citations 
PageRank 
References 
2
0.36
15
Authors
2
Name
Order
Citations
PageRank
Diego García-Saiz15710.32
Marta E. Zorrilla25116.05