An Investigation on the Compression Quality of aiNet

AiNet is an immune-inspired algorithm for data compression, i.e. the reduction of redundancy in data sets. In this paper we investigate the compression quality of aiNet. Therefore, a similarity measure between input set and reduced output set is presented which is based on the Parzen window estimation and the Kullback-Leibler divergence. Four different artificially generated data sets are created and the compression quality is investigated. Experiments reveal that aiNet produced reasonable results on an uniformly distributed data set, but poor results on non-uniformly distributed data sets, i.e. data sets which contain dense point regions. This effect is caused by the optimization criterion of aiNet.

• Type: Conference
• Year: 2007
• Where: FOCI
• Authors: Thomas Stibor, Jonathan Timmis
• Keywords: Applied Computing, Compression Quality, Data Compression, Data Sets, FOCI 2007