PatHT: An Efficient Method of Classification over Evolving Data Streams

Some existing classifications need frequent update to adapt to the change of concept in data streams. To solve this problem, an adaptive method Pattern-based Hoeffding Tree (PatHT) is proposed to process evolving data streams. A key technology of a training classification decision tree is to improve the efficiency of choosing an optimal splitting attribute. Therefore, frequent patterns are used. Algorithm PatHT discovers constraint based closed frequent patterns incremental updated. It builds an adaptive and incremental updated tree based on the frequent pattern set. It uses sliding window to avoid concept drift in mining patterns and uses concept drift detector to deal with concept change problem in procedure of training examples. We tested the performance of PatHT against some known algorithms using real data streams and synthetic data streams with different widths of concept change. Our approach outperforms traditional classification models and it is proved by the experimental results