An efficient continuous attributes handling method for mining concept-drifting data streams based on skip list

Authors:
Zhenzheng Ouyang;Yuhai Gao;Mingjun Li;Jianshu Luo
Affiliations:
School of Science, National University of Defense Technology, Changsha, China;School of Science, National University of Defense Technology, Changsha, China;Army Unit 63961, Beijing, China;School of Science, National University of Defense Technology, Changsha, China
Venue:
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Year:
2011

Citing 9
Cited 0

Learning in the presence of concept drift and hidden contexts

Machine Learning
Tolerating Concept and Sampling Shift in Lazy Learning UsingPrediction Error Context Switching

Artificial Intelligence Review - Special issue on lazy learning
Extracting Hidden Context

Machine Learning - Special issue on context sensitivity and concept drift
Mining high-speed data streams

Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining time-changing data streams

Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Incremental Learning from Noisy Data

Machine Learning
Mining concept-drifting data streams using ensemble classifiers

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning drifting concepts: Example selection vs. example weighting

Intelligent Data Analysis
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts

The Journal of Machine Learning Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper focuses on continuous attributes handling for mining data stream with concept drift. CVFDT is one of the most successful methods for handling concept drift efficiently. In this paper, we revisit this problem and present an algorithm named SL_CVFDT on top of CVFDT. It is fast as hash table when inserting, seeking or deleting attribute value, and it also can sort the attribute value. The average time cost of search, insertion and deletion is O(log2n),and average memory cost of point is O(n).At the same time, it can get best split point just traverse the skip list once.