ATISA: Adaptive Threshold-based Instance Selection Algorithm

作者：Cavalcanti George D C^*; Ren Tsang Ing; Pereira Cesar Lima

来源：Expert Systems with Applications, 2013, 40(17): 6894-6900.

DOI：10.1016/j.eswa.2013.06.053

• Instance selection
• Instance-based learning algorithms

Instance reduction techniques can improve generalization, reduce storage requirements and execution time of instance-based learning algorithms. This paper presents an instance reduction algorithm called Adaptive Threshold-based Instance Selection Algorithm (ATISA). ATISA aims to preserve important instances based on a selection criterion that uses the distance of each instance to its nearest enemy as a threshold. This threshold defines the coverage area of each instance that is given by a hyper-sphere centered at it. The experimental results show the effectiveness, in terms of accuracy, reduction rate, and computational time, of the ATISA algorithm when compared with state-of-the-art reduction algorithms.

• 出版日期2013-12-1