Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (1): 252-260.

Incremental Integration Algorithm based on Incremental RLID3

Hongbin Wanga, Lei Hub, Xiaodong Xiea, Lianke Zhoua*(), and Huafeng Lia

1. a College of Computer Science and Technology, Harbin Engineering University, Harbin, 150001,China
b General Office, Systems Engineering Research Institute, Beijing, 100094, China
•  Revised on  ; Accepted on
• Contact: Zhou Lianke E-mail:zhoulianke@hrbeu.edu.cn
• About author:Hongbin Wang received his PhD degree in computer application technology from Harbin Engineering University in 2010. He is currently an associate professor in the College of Computer Science and Technology of Harbin Engineering University. His research interests include information management, data integration, data space, semantic web, ontology and information system design.|Lei Hu received his Master’s degree in computer application technology from Wuhan University in 2004. He is currently a senior engineer of SERI. His research interests include information management, systems engineering, and system integration.|Xiaodong Xie is now studying for a Bachelor’s degree in computer application technology at the Harbin Engineering University in 2018. His research interests include deep learning, big data, AI, machine learning.|Lianke Zhou received his PhD degree in computer architecture from Harbin Institute of Technology in 2011. He is currently a lecturer in the College of Computer Science and Technology of Harbin Engineering University. His research interests include data visualization, dataspace, distributed computing and mobile computing.|Huafeng Li received his master degree in software engineering from Harbin Engineering University in 2016.His research interests include information management, data integration, and data classification.

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.