A Noise Resilient and Non-parametric Graph-based Classifier

Mahdi Mohammadi, Saeed Adel Mehraban, Elnaz Bigdeli, Bijan Raahemi, Ahmad Akbari

2014

Abstract

In this paper, we propose a non-parametric and noise resilient graph-based classification algorithm. In designing the proposed method, we represent each class of dataset as a set of sub-graphs. The main part of the training phase is how to build the classification graph based on the non-parametric k-associated optimal graph algorithm which is an extension of the parametric k-associated graph algorithm. In this paper, we propose a new extension and modification of the training phase of the k-associated optimal graph algorithm. We compare the modified version of the k-associated optimal graph (MKAOG) algorithm with the original k-associated optimal graph algorithm (KAOG). The experimental results demonstrate superior performance of our proposed method in the presence of different levels of noise on various datasets from the UCI repository.

References

  1. Belkin, P. Niyogi, Laplacian eigenmaps for dimensionality reduction and data representation, Neural Computation 15 (2003) 1373-1396.
  2. Bertini Jr, Liang Zhao, Robson Motta, Alneu de Andrade Lopes, 'A nonparametric classification method based on K-associated graphs', Information Sciences 181 (2011) 5435-5456.
  3. Chatterjee A, Raghava P, 'Similarity Graph Neighborhoods for Enhanced Supervised Classification', Procedia Computer Science, Volume 9, 2012, Pages 577-586.
  4. Chen, L. Li, J. Peng, Error bounds of multi-graph regularized semi-supervised classification, Information Sciences 179 (2009) 1960-1969.
  5. Deshpande, M. Kuramochi, and G. Karypis, “Frequent Sub-Structure-Based Approaches for Classifying Chemical Compounds,” IEEE Transactionson Knowledge and Data Engineering, vol. 17, no. 8, pp. 1036- 1050, 2005.
  6. Dhanjala C, Gaudelb R, Clémençonc S, 'Efficient eigenupdating for spectral graph clustering', Neurocomputing, Volume 131, 5 May 2014, Pages 440-452.
  7. Gonzalez, L. Holder, and D. Cook, “Graph-based relational concept learning,” Proceedings of the Nineteenth International Conference on Machine Learning, 2002.
  8. Jun Ye, Zhong Jin, “Dual-graph regularized Concept Factorization for Clustering”, Neurocomputing, In Press, Accepted Manuscript, Available online 13 April 2014.
  9. Ketkar, N.S. ; Holder, L.B. ; Cook, D.J.,78 Empirical comparison of graph classification algorithms', Computational Intelligence and Data Mining, 2009. CIDM 7809. 259 - 266.
  10. Marios Iliofotou, Hyun-chul Kim, Michalis Faloutsos, Michael Mitzenmacher, Prashanth Pappu, George Varghese, 'Graption: A graph-based P2P traffic classification framework for the internet backbone', Computer Networks 55 (2011) 1909-1920.
  11. Vathy-Fogarassy, J. Abonyi, Local and global mappings of topology representing networks, Information Sciences 179 (2009) 3791-3803.
  12. Zhu X, Semi-Supervised Learning Literature Survey, Technical Report 1530, Computer-Science, University of Wisconsin-Madison, 2008.
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Paper Citation


in Harvard Style

Mohammadi M., Adel Mehraban S., Bigdeli E., Raahemi B. and Akbari A. (2014). A Noise Resilient and Non-parametric Graph-based Classifier . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014) ISBN 978-989-758-048-2, pages 170-175. DOI: 10.5220/0005051801700175


in Bibtex Style

@conference{kdir14,
author={Mahdi Mohammadi and Saeed Adel Mehraban and Elnaz Bigdeli and Bijan Raahemi and Ahmad Akbari},
title={A Noise Resilient and Non-parametric Graph-based Classifier},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014)},
year={2014},
pages={170-175},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005051801700175},
isbn={978-989-758-048-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014)
TI - A Noise Resilient and Non-parametric Graph-based Classifier
SN - 978-989-758-048-2
AU - Mohammadi M.
AU - Adel Mehraban S.
AU - Bigdeli E.
AU - Raahemi B.
AU - Akbari A.
PY - 2014
SP - 170
EP - 175
DO - 10.5220/0005051801700175