Username   Password       Forgot your password?  Forgot your username? 

 

Incremental Integration Algorithm based on Incremental RLID3

Volume 15, Number 1, January 2019, pp. 252-260
DOI: 10.23940/ijpe.19.01.p25.252260

Hongbin Wanga, Lei Hub, Xiaodong Xiea, Lianke Zhoua, and Huafeng Li

aCollege of Computer Science and Technology, Harbin Engineering University, Harbin, 150001, China
bGeneral Office, Systems Engineering Research Institute, Beijing, 100094, China

(Submitted on October 12, 2018; Revised on November 11, 2018; Accepted on December 23, 2018)

Abstract:

In the research process of ID3 algorithm, some deficiencies were found. RLID3 algorithm is on the improvement of ID3 algorithm in terms of the number of leaf nodes. RLID3 algorithm uses ensemble learning method to integrate multiple incremental RLID3 model and the predictive ability of the algorithm is further improved. Incre_RLID3 is an incremental learning algorithm that is based on a decision tree constructed by RLID3. It adjusts construction of the tree using incremental data set. The goal of this algorithm is to use the new data on the basis of the original decision tree. In order to further improve the accuracy of the algorithm, this paper proposes an ensemble algorithm PAR_WT. The basic idea of this algorithm is to use the data set to generate multiple RLID3 decision tree. Then, the test samples are classified by each decision tree. Finally, combined with the PAR_WT algorithm and Incre_RLID3 algorithm, an incremental ensemble algorithm Incre_RLID3_ENM algorithm with incremental learning ability is obtained.

 

References: 11

      1. C. L. Cassano, A. J. Simon, L. Wei, C. Fredrickson, and Z. H. Fan, “Use of Vacuum Bagging for Fabricating Thermoplastic Microfluidic Devices,” Lab on a Chip, Vol. 15, No. 1, pp. 62, 2015
      2. Y. Chen, J. Cao, S. Feng, and Y. Tan, “An Ensemble Learning based Approach for Building Airfare Forecast Service,” in Proceedings of IEEE International Conference on Big Data, pp. 964-969, 2015
      3. B. Gu, V. S. Sheng, Z. Wang, D. Ho, S. Osman, and S. Li, “Incremental Learning for ν-Support Vector Regression,” Neural Networks, Vol. 67, No. C, pp. 140, 2015
      4. Y. Guo, L. Jiao, S. Wang, S. Wang, F. Liu, K. Rong, et al., “A Novel Dynamic Rough Subspace based Selective Ensemble,” Pattern Recognition, Vol. 48, No. 5, pp. 1638-1652, 2015
      5. B. Han, B. He, N. Rui, M. Ma, S. Zhang, M. Li, et al., “LARSEN-ELM: Selective Ensemble of Extreme Learning Machines using LARS for Blended Data,” Neurocomputing, Vol. 149, pp. 285-294, 2015
      6. L. Hu, C. Shao, J. Li, and H. Ji, “Incremental Learning from News Events,” Knowledge-based Systems, Vol. 89, No. C, pp. 618-626, 2015
      7. N. Li, Y. Jiang, and Z. H. Zhou, “Multi-Label Selective Ensemble,” in Proceedings of International Workshop on Multiple Classifier Systems, pp. 76-88, 2015
      8. D. Peressutti, W. Bai, T. Jackson, M. Sohal, A. Rinaldi, D. Rueckert, et al., “Prospective Identification of CRT Super Responders using a Motion Atlas and Random Projection Ensemble Learning,” in Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 493-500, 2015
      9. J. C. Schlimmer and D. H. Fisher, “A Case Study of Incremental Concept Induction,” in Proceedings of National Conference on Artificial Intelligence, pp. 496-501, Philadelphia, Pa, August 11-15, 1986
      10. X. Z. Wang, H. J. Xing, Y. Li, Q. Hua, C. R. Dong, and W. Pedrycz, “A Study on Relationship Between Generalization Abilities and Fuzziness of Base Classifiers in Ensemble Learning,” IEEE Transactions on Fuzzy Systems, Vol. 23, No. 5, pp. 1638-1654, 2015
      11. M. S. Zia and M. A. Jaffar, “An Adaptive Training based on Classification System for Patterns in Facial Expressions using SURF Descriptor Templates,” Kluwer Academic Publishers, 2015

           

          Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

           
          This site uses encryption for transmitting your passwords. ratmilwebsolutions.com