关键词: 属性异常点, 人工蜂群算法, 模拟退火, O-measure

Abstract: In order to solve the problems of high time complexity, low accuracy and low recall in detecting anomaly database attributes, this paper proposes a new method based on ABC (artificial bee colony) and the O-measure metric (i.e., a kind of attribute outlier evaluation metric) to find out the attribute outliers, which simulates the bee colony  behavior of searching for high quality food sources. In view of the local convergence of swarm intelligence algorithm to detect the attribute outliers, this paper presents the approach of finding the global optimal solution by using the simulated annealing technique, making the bee swarm jump out of the local optimal solution. The proposed algorithm calculates the O-measure of each attribute that the bees have walked, and then from the O-measure value result sets, chooses the best food sources (i.e., the attribute outliers). In comparison with other algorithms, the experimental results show that the proposed algorithm needs less time, and improves the detection precision and recall.

Key words: attribute outlier, artificial bee colony algorithm, simulated annealing, O-measure