Dimensionality Reduction for Supervised Learning in Link Prediction Problems

Antonio Pecli; Bruno Giovanini; Carla C. Pacheco; Carlos Moreira; Fernando Ferreira; Frederico Tosta; Júlio Tesolin; Marcio Vinicius Dias; Silas Filho; Maria Claudia Cavalcanti; Ronaldo Goldschmidt

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Paper

Dimensionality Reduction for Supervised Learning in Link Prediction Problems

Topics: Data Mining

In Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 2: ICEIS, 295-302, 2015 , Barcelona, Spain

Authors: Antonio Pecli ; Bruno Giovanini ; Carla C. Pacheco ; Carlos Moreira ; Fernando Ferreira ; Frederico Tosta ; Júlio Tesolin ; Marcio Vinicius Dias ; Silas Filho ; Maria Claudia Cavalcanti and Ronaldo Goldschmidt

Affiliation: Military Institute of Engineering, Brazil

Keyword(s): Link Prediction, Supervised Learning, Machine Learning, Dimensionality Reduction.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Sensor Networks ; Signal Processing ; Soft Computing

Abstract: In recent years, a considerable amount of attention has been devoted to research on complex networks and their properties. Collaborative environments, social networks and recommender systems are popular examples of complex networks that emerged recently and are object of interest in academy and industry. Many studies model complex networks as graphs and tackle the link prediction problem, one major open question in network evolution. It consists in predicting the likelihood of an association between two not interconnected nodes in a graph to appear. One of the approaches to such problem is based on binary classification supervised learning. Although the curse of dimensionality is a historical obstacle in machine learning, little effort has been applied to deal with it in the link prediction scenario. So, this paper evaluates the effects of dimensionality reduction as a preprocessing stage to the binary classifier construction in link prediction applications. Two dimensionality reduct ion strategies are experimented: Principal Component Analysis (PCA) and Forward Feature Selection (FFS). The results of experiments with three different datasets and four traditional machine learning algorithms show that dimensionality reduction with PCA and FFS can improve model precision in this kind of problem. (More)

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Paper citation in several formats:

Pecli, A.; Giovanini, B.; C. Pacheco, C.; Moreira, C.; Ferreira, F.; Tosta, F.; Tesolin, J.; Vinicius Dias, M.; Filho, S.; Claudia Cavalcanti, M. and Goldschmidt, R. (2015). Dimensionality Reduction for Supervised Learning in Link Prediction Problems. In Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 2: ICEIS; ISBN 978-989-758-096-3; ISSN 2184-4992, SciTePress, pages 295-302. DOI: 10.5220/0005371802950302

@conference{iceis15,
author={Antonio Pecli. and Bruno Giovanini. and Carla {C. Pacheco}. and Carlos Moreira. and Fernando Ferreira. and Frederico Tosta. and Júlio Tesolin. and Marcio {Vinicius Dias}. and Silas Filho. and Maria {Claudia Cavalcanti}. and Ronaldo Goldschmidt.},
title={Dimensionality Reduction for Supervised Learning in Link Prediction Problems},
booktitle={Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 2: ICEIS},
year={2015},
pages={295-302},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005371802950302},
isbn={978-989-758-096-3},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 2: ICEIS
TI - Dimensionality Reduction for Supervised Learning in Link Prediction Problems
SN - 978-989-758-096-3
IS - 2184-4992
AU - Pecli, A.
AU - Giovanini, B.
AU - C. Pacheco, C.
AU - Moreira, C.
AU - Ferreira, F.
AU - Tosta, F.
AU - Tesolin, J.
AU - Vinicius Dias, M.
AU - Filho, S.
AU - Claudia Cavalcanti, M.
AU - Goldschmidt, R.
PY - 2015
SP - 295
EP - 302
DO - 10.5220/0005371802950302
PB - SciTePress