Abstract
Machine Learning is an increasing area over the last few years and it is one of the highlights in Artificial Intelligence area. Nowadays, one of the most studied areas is Semi-supervised learning, mainly due to its characteristic of lower cost in labeling sample data. The most active category in this subarea is that of graph-based models. The Particle Competition and Cooperation in Networks algorithm is one of the techniques in this field, which has always used the Euclidean distance to measure the similarity between data and to build the graph. This project aims to implement the algorithm and apply other distance metrics in it, over different datasets. Thus, the results on these metrics are compared to analyze if there is such a metric that produces better results, or if different datasets require a different metric in order to obtain a better correct classification rate. We also expand this gained knowledge, proposing how to identify the best metric for the algorithm based on its initial graph structure, with no need to run the algorithm for each metric we want to evaluate.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alpaydin, E.: Introduction to Machine Learning. MIT Press, Cambridge (2004)
Mitchell, T.: Machine Learning. McGraw Hill, New York (1997)
Chapelle, O., Schölkopf, B., Zien, A.: Semi-Supervised Learning, Adaptive Computation and Machine Learning. MIT Press, Cambridge (2006)
Breve, F.A., Zhao, L., Quiles, M.G., Pedrycz, W., Liu, J.: Particle competition and cooperation in networks for semi-supervised learning. IEEE Trans. Knowl. Data Eng. 24, 1686–1698 (2012)
Liu, Q., Chu, X., Xiao, J., Zhu, H.: Optmizing non-orthogonal space distance using PSO in software cost estimation. In: IEEE Computer Software Applications Conference (COMPSAC), pp. 21–26 (2014)
Yang, Z., Shufan, Y., Yang, X., Liqun, G.: High-dimensional statistical distance for object tracking. In: International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), vol. 2, pp. 386–389 (2010)
Shyam, R., Singh, Y.N.: Evaluation of eigenface and fisherfaces using Bray Curtis dissimilarity metric. In: 9th International Conference on Industrial and Information Systems (ICIIS), pp. 1–6 (2014)
Kokare, M., Chatterji, B.N., Biswas, P.K.: Comparison of similarity metrics for texture image retrieval. In: Conference on Convergent Technologies for the Asia-Pacific Region, vol. 2, pp. 571–575 (2003)
Bache, K., Lichman, M.: UCI Machine Learning Repository http://archive.ics.uci.edu/ml. Irvine, CA. University of California, School of Information and Computer Science (2013)
Acknowledgment
The authors would like to thank the São Paulo Research Foundation - FAPESP (grant #2016/05669-4) and the National Counsel of Technological and Scientific Development - CNPq (grant #475717/2013-9) for the financial support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Guerreiro, L., Breve, F. (2017). Analyzing and Inferring Distance Metrics on the Particle Competition and Cooperation Algorithm. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10409. Springer, Cham. https://doi.org/10.1007/978-3-319-62407-5_53
Download citation
DOI: https://doi.org/10.1007/978-3-319-62407-5_53
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-62406-8
Online ISBN: 978-3-319-62407-5
eBook Packages: Computer ScienceComputer Science (R0)