The week's pick
Random Articles
Reseach Article
The Performance of Single Classifier, Ensemble without Diversity and Ensemble with Diversity
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 181 - Number 16 |
| Year of Publication: 2018 |
| Authors: Imad Alhadi Ganan, Vladislav Miskovic |
10.5120/ijca2018917782
|
Imad Alhadi Ganan, Vladislav Miskovic . The Performance of Single Classifier, Ensemble without Diversity and Ensemble with Diversity. International Journal of Computer Applications. 181, 16 ( Sep 2018), 31-34. DOI=10.5120/ijca2018917782
Abstract
The performance of ensemble depends on the single classifiers chosen. Diversity in ensemble could be a factor that may influence the results or the performance of ensemble. In this study we have employed bagging and boosting as ensemble classifier, DECORATE to tackle diversity in ensemble. We have chosen random forest, random tree, j48 and j48 grafts mainly as a base classifier for the ensemble methods. The empirical evidence has shown that Boosting algorithm without diversity do not improve the test performance of the single classifier.
References
- M. Kusum and M. Rupali, “A REVIEW ON VARIOUS CLASSIFICATION ALGORITHMS FOR AN INCREMENTAL SPAM FILTER,” Int. J. Appl. or Innov. Eng. Manag., vol. 2, no. 11, pp. 325–331, 2013.
- C. Zhang and Y. Ma, Ensemble Machine Learning: Methods and Applications. New York: Springer New York, 2012.
- D. Che, Q. Liu, K. Rasheed, and X. Tao, “Software Tools and Algorithms for Biological Systems,” Adv. Exp. Med. Biol., vol. 696, no. 1, pp. 191–199, 2011.
- D. Darwish, “Data Mining: Concepts, Models, Methods, and Algorithms,” Int. J. Comput. Sci., vol. 10, no. 4, pp. 103–111, 2013.
- S. Kulkarni and V. Kelkar, “Classification of Multispectral Satellite Images Using Ensemble Techniques of Bagging , Boosting and Ada- Boost,” pp. 253–258, 2014.
- M. M. Fraz et al., “Retinal vessel segmentation using ensemble classifier of bagged decision trees,” IET Conf. Image Process. (IPR 2012), pp. B9–B9, 2012.
- R. Ye and P. N. Suganthan, “Empirical Comparison of Bagging-based Ensemble Classifiers,” Int. J. Inf. Technol. Converg. Serv., vol. 1, no. 1, pp. 917–924, 2013.
- R. E. Schapire, “Explaining AdaBoost,” Princeton University, 2010.
- N. García-Pedrajas and A. de Haro-García, “Boosting instance selection algorithms,” Knowledge-Based Syst., vol. 67, pp. 342–360, Sep. 2014.
- E. Rashedi and A. Mirzaei, “A hierarchical clusterer ensemble method based on boosting theory,” Knowledge-Based Syst., vol. 45, pp. 83–93, Jun. 2013.
- B. Krawczyk and M. Woźniak, “Diversity measures for one-class classifier ensembles,” Neurocomputing, vol. 126, pp. 36–44, Feb. 2014.
- D. S. C. Nascimento, A. L. V. Coelho, and A. M. P. Canuto, “Integrating complementary techniques for promoting diversity in classifier ensembles: A systematic study,” Neurocomputing, vol. 138, pp. 347–357, Aug. 2014.
- J. Stefanowski and M. Pachocki, “Comparing Performance of Committee Based Approaches to Active Learning,” Recent Adv. Intell. Inf. Syst., vol. 1, no. 1998, pp. 457–470, 2013.
Index Terms
Keywords