Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

Semi-supervised Classification: Cluster and Label Approach using Particle Swarm Optimization

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Shahira Shaaban Azab, Mohamed Farouk Abdel Hady, Hesham Ahmed Hefny
10.5120/ijca2017913013

Shahira Shaaban Azab, Mohamed Farouk Abdel Hady and Hesham Ahmed Hefny. Semi-supervised Classification: Cluster and Label Approach using Particle Swarm Optimization. International Journal of Computer Applications 160(3):39-44, February 2017. BibTeX

@article{10.5120/ijca2017913013,
	author = {Shahira Shaaban Azab and Mohamed Farouk Abdel Hady and Hesham Ahmed Hefny},
	title = {Semi-supervised Classification: Cluster and Label Approach using Particle Swarm Optimization},
	journal = {International Journal of Computer Applications},
	issue_date = {February 2017},
	volume = {160},
	number = {3},
	month = {Feb},
	year = {2017},
	issn = {0975-8887},
	pages = {39-44},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume160/number3/27056-2017913013},
	doi = {10.5120/ijca2017913013},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Classification predicts classes of objects using the knowledge learned during the training phase. This process requires learning from labeled samples. However, the labeled samples usually limited. Annotation process is annoying, tedious, expensive, and requires human experts. Meanwhile, unlabeled data is available and almost free. Semi-supervised learning approaches make use of both labeled and unlabeled data. This paper introduces cluster and label approach using PSO for semi-supervised classification. PSO is competitive to traditional clustering algorithms. A new local best PSO is presented to cluster the unlabeled data. The available labeled data guides the learning process. The experiments are conducted using four state-of-the-art datasets from different domains. The results compared with Label Propagation a popular semi-supervised classifier and two state-of-the-art supervised classification models, namely k-nearest neighbors and decision trees. The experiments show the efficiency of the proposed model.

References

  1. X. Zhu and A. B. Goldberg, “Introduction to semi-supervised learning,” Synth. Lect. Artif. Intell. Mach. Learn., vol. 3, no. 1, 2009.
  2. S. Ebert and B. Schiele, “Where Next in Object Recognition and how much Supervision Do We Need?,” in Advanced Topics in Computer Vision, Springer, 2013, pp. 35–64.
  3. M. Li and Z.-H. Zhou, “Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples,” Syst. Man Cybern. Part A Syst. Humans, IEEE Trans., vol. 37, no. 6, pp. 1088–1098, 2007.
  4. C. C. Aggarwal, “An Introduction to Data Classification,” in Data Classification: Algorithms and Applications, C. C. Aggarwal, Ed. CRC Press, 2015, pp. 1–36.
  5. B. M. Shahshahani and D. A. Landgrebe, “The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon,” Geosci. Remote Sensing, IEEE Trans., vol. 32, no. 5, pp. 1087–1095, 1994.
  6. O. Chapelle, B. Schölkopf, and A. Zien, Semi-supervised learning. MIT press Cambridge, 2006.
  7. K. Sinha, “Semi-Supervised Learning,” in Data Classification: Algorithms and Applications, C. C. Aggarwal, Ed. CRC Press, 2015, pp. 511–536.
  8. I. Triguero, S. García, and F. Herrera, “Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study,” Knowl. Inf. Syst., vol. 2, no. 42, pp. 245–284, 2015.
  9. I. De Falco, A. Della Cioppa, and E. Tarantino, “Facing classification problems with Particle Swarm Optimization,” Appl. Soft Comput., vol. 7, no. 3, pp. 652–658, Jun. 2007.
  10. N. Nouaouria, M. Boukadoum, and R. Proulx, “Particle swarm classification: A survey and positioning,” Pattern Recognit., vol. 46, no. 7, pp. 2028–2044, Jul. 2013.
  11. T. Sousa, A. Silva, and A. Neves, “Particle swarm based data mining algorithms for classification tasks,” Parallel Comput., vol. 30, no. 5, pp. 767–783, 2004.
  12. S. Mahapatra, A. K. Jagadev, and B. Naik, “Performance Evaluation of PSO Based Classifier for Classification of Multidimensional Data with Variation of PSO Parameters in Knowledge Discovery Database,” vol. 34, pp. 27–44, 2011.
  13. M. Omran, A. Salman, and A. P. Engelbrecht, “Image classification using particle swarm optimization,” in the Fourth Asia–Pacific Conference on Simulated Evolution and Learning, 2002, pp. 18–22.
  14. A. Cervantes, I. M. Galván, and P. Isasi, “AMPSO : A New Particle Swarm Method for Nearest Neighborhood Classification,” IEEE Trans. Syst. Man. Cybern., vol. 39, no. 5, pp. 1082–1091, 2009.
  15. G. W. Jiji and P. J. DuraiRaj, “Content-Based Image Retrieval Techniques for the Analysis of Dermatological Lesions Using Particle Swarm Optimization Technique,” Appl. Soft Comput., Feb. 2015.
  16. D. Van Der Merwe and A. Engelbrecht, “Data Clustering using Particle Swarm Optimization,” 2003 Ieee, pp. 215–220, 2003.
  17. S. Shaaban Azab, M. F. A. Hady, and H. A. Hefny, “Local Best Particle Swarm Optimization for Partitioning Data Clustering,” 12th International Computer Engineering Conference (ICENCO2016), 2016.
  18. C. L. Blake and C. J. Merz, “UCI Repository of machine learning databases [http://www. ics. uci. edu/~ mlearn/MLRepository. html]. Irvine, CA: University of California,” Dep. Inf. Comput. Sci., vol. 55, 1998.

Keywords

Swarm intelligence, Classification, Clustering, Semi-supervised, Cluster and label.