Neighborhood Random Classification
Ensemble methods (EMs) have become increasingly popular in data mining because of their efficiency. These methods generate a set of classifiers using one or several machine learning algorithms (MLAs) and aggregate them into a single classifier (Meta-Classifier, MC). Decision trees (DT), SVM and k-Nearest Neighbors (kNN) are among the most well-known used in the context of EMs. Here, we propose an approach based on neighborhood graphs as an alternative. Thanks to these related graphs, like relative neighborhood graphs (RNGs), Gabriel graphs (GGs) or Minimum Spanning Tree (MST), we provide a generalized approach to the kNN approach with less arbitrary parameters such as the value of k. Neighborhood graphs have never been introduced into EM approaches before. The results of our algorithm : Neighborhood Random Classification are very promising as they are equal to the best EM approaches such as Random Forest or those based on SVMs. In this preliminary and experimental work, we provide the methodological approach and many comparative results. We also provide some results on the influence of neighborhood structure regarding the efficiency of the classifier and draw some issues that deserves to be studied.
KeywordsEnsemble methods neighborhood graphs relative neighborhood Graphs Gabriel Graphs k-Nearest Neighbors
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- 1.Breiman, L.: Bias, variance, and arcing classifiers. Statistics (1996)Google Scholar
- 4.Domingos, P.: A unified bias-variance decomposition and its applications. In: ICML, pp. 231–238. Citeseer (2000)Google Scholar
- 5.Ham, J., Chen, Y., Crawford, M., Ghosh, J.: Investigation of the random forest framework for classification of hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing 43(3) (2005)Google Scholar
- 6.Ho, T., Kleinberg, E.: Building projectable classifiers of arbitrary complexity. In: International Conference on Pattern Recognition, vol. 13, pp. 880–885 (1996)Google Scholar
- 7.Kohavi, R., Wolpert, D.: Bias plus variance decomposition for zero-one loss functions. In: Machine Learning-International Workshop, pp. 275–283. Citeseer (1996)Google Scholar
- 8.O’Mahony, M.P., Cunningham, P., Smyth, B.: An assessment of machine learning techniques for review recommendation. In: Coyle, L., Freyne, J. (eds.) AICS 2009. LNCS, vol. 6206, pp. 241–250. Springer, Heidelberg (2010), http://portal.acm.org/citation.cfm?id=1939047.1939075 CrossRefGoogle Scholar
- 11.Preparata, F., Shamos, M.: Computational geometry: an introduction. Springer (1985)Google Scholar
- 12.Schapire, R.: The boosting approach to machine learning: An overview. Lecture Notes In Statistics, pp. 149–172. Springer (2003)Google Scholar
- 15.Wang, X., Tang, X.: Random sampling lda for face recognition, pp. 259–267 (2004), http://portal.acm.org/citation.cfm?id=1896300.1896337