An Investigation on the Compression Quality of aiNet

Thomas Stibor, Jonathan Timmis
2007 2007 IEEE Symposium on Foundations of Computational Intelligence  
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
more » ... ced 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.
doi:10.1109/foci.2007.371518 dblp:conf/foci/StiborT07 fatcat:oyqw5liig5gfdislyubr2saqbu