Heterogeneous Ensemble for Imaginary Scene Classification

Saleh Alyahyan, Majed Farrash, Wenjia Wang

Abstract

In data mining, identifying the best individual technique to achieve very reliable and accurate classification has always been considered as an important but non-trivial task. This paper presents a novel approach - heterogeneous ensemble technique, to avoid the task and also to increase the accuracy of classification. It combines the models that are generated by using methodologically different learning algorithms and selected with different rules of utilizing both accuracy of individual modules and also diversity among the models. The key strategy is to select the most accurate model among all the generated models as the core model, and then select a number of models that are more diverse from the most accurate model to build the heterogeneous ensemble. The framework of the proposed approach has been implemented and tested on a real-world data to classify imaginary scenes. The results show our approach outperforms other the state of the art methods, including Bayesian network, SVM and AdaBoost.

References

  1. Bosch, A., Zisserman, A., and Mu n╦ťoz, X. (2006). Scene classification via plsa. In Computer Vision-ECCV 2006, pages 517-530. Springer.
  2. Brown, G., Wyatt, J., Harris, R., and Yao, X. (2005). Diversity creation methods: a survey and categorisation. Information Fusion, 6(1):5-20.
  3. Caruana, R., Niculescu-Mizil, A., Crew, G., and Ksikes, A. (2004). Ensemble selection from libraries of models. In Proceedings of the twenty-first international conference on Machine learning, page 18. ACM.
  4. Dietterich, T. G. (2000). Ensemble methods in machine learning, pages 1-15. Springer.
  5. Giacinto, G. and Roli, F. (2001). Design of effective neural network ensembles for image classification purposes. Image and Vision Computing, 19(9):699-707.
  6. Grauman, K. and Darrell, T. (2005). The pyramid match kernel: Discriminative classification with sets of image features. In Computer Vision, 2005. ICCV 2005.
  7. Ke, Y. and Sukthankar, R. (2004). Pca-sift: A more distinctive representation for local image descriptors. In Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on, volume 2, pages II-506. IEEE.
  8. Lazebnik, S., Schmid, C., and Ponce, J. (2006). Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, volume 2, pages 2169-2178. IEEE.
  9. Lertampaiporn, S., Thammarongtham, C., Nukoolkit, C., Kaewkamnerdpong, B., and Ruengjitchatchawalya, M. (2013). Heterogeneous ensemble approach with discriminative features and modified-smotebagging for pre-mirna classification. Nucleic acids research, 41(1):e21-e21.
  10. Liu, Y., Yao, X., and Higuchi, T. (2000). Evolutionary ensembles with negative correlation learning. Evolutionary Computation, IEEE Transactions on, 4(4):380- 387.
  11. Lowe, D. G. (2004). Distinctive image features from scaleinvariant keypoints. International Journal of Computer Vision, 60(2):91-110.
  12. Mikolajczyk, K. and Schmid, C. (2004). Scale & affine invariant interest point detectors. International Journal of Computer Vision, 60(1):63-86.
  13. Oliva, A. and Torralba, A. (2001). Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision, 42(3):145-175.
  14. Partridge, D. and Krzanowski, W. (1997). Software diversity: practical statistics for its measurement and exploitation. Information and software technology, 39(10):707-717.
  15. Siagian, C. and Itti, L. (2007). Rapid biologically-inspired scene classification using features shared with visual attention. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 29(2):300-312.
  16. Wallraven, C., Caputo, B., and Graf, A. (2003). Recognition with local features: the kernel recipe. In Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on, pages 257-264. IEEE.
  17. Wang, H., Fan, W., Yu, P. S., and Han, J. (2003). Mining concept-drifting data streams using ensemble classifiers. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 226-235. ACM.
  18. Wang, W. (2008). Some fundamental issues in ensemble methods. In Neural Networks, 2008. IJCNN 2008.(IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on, pages 2243-2250. IEEE.
  19. Yan, R., Liu, Y., Jin, R., and Hauptmann, A. (2003). On predicting rare classes with svm ensembles in scene classification. In Acoustics, Speech, and Signal Processing, 2003. Proceedings.(ICASSP'03). 2003 IEEE International Conference on, volume 3, pages III-21. IEEE.
  20. Yang, J., Jiang, Y.-G., Hauptmann, A. G., and Ngo, C.-W. (2007). Evaluating bag-of-visual-words representations in scene classification. In Proceedings of the international workshop on Workshop on multimedia information retrieval, pages 197-206. ACM.
  21. Zenobi, G. and Cunningham, P. (2001). Using diversity in preparing ensembles of classifiers based on different feature subsets to minimize generalization error, pages 576-587. Springer.
  22. Zhang, S., Cohen, I., Goldszmidt, M., Symons, J., and Fox, A. (2005). Ensembles of models for automated diagnosis of system performance problems. In Dependable Systems and Networks, 2005. DSN 2005. Proceedings. International Conference on, pages 644- 653. IEEE.
Download


Paper Citation


in Harvard Style

Alyahyan S., Farrash M. and Wang W. (2016). Heterogeneous Ensemble for Imaginary Scene Classification . In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016) ISBN 978-989-758-203-5, pages 197-204. DOI: 10.5220/0006037101970204


in Bibtex Style

@conference{kdir16,
author={Saleh Alyahyan and Majed Farrash and Wenjia Wang},
title={Heterogeneous Ensemble for Imaginary Scene Classification},
booktitle={Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)},
year={2016},
pages={197-204},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006037101970204},
isbn={978-989-758-203-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)
TI - Heterogeneous Ensemble for Imaginary Scene Classification
SN - 978-989-758-203-5
AU - Alyahyan S.
AU - Farrash M.
AU - Wang W.
PY - 2016
SP - 197
EP - 204
DO - 10.5220/0006037101970204