A Data Classifier Based on Maximum Likelihood Evidential Reasoning Rule

In Dempster–Shafer evidence theory (DST), some classical combination rules can be used to fuse the multiple pieces of evidence, respectively abstracted from different attributes (features) so as increase accuracy multiattribute classification decision making. However, most them have not yet considered interdependence among evidence. The newly proposed maximum likelihood evidential reasoning (MAKER) rule measures such ubiquitous by introducing correlation factors into combination. Hence, this paper designs a MAKER-based classifier mine more information for data classification. Finally, numerical analysis (classification) experiments are carried out using five popular benchmark databases University California, Irvine (UCI) illustrate that refined measure aggregate fused probability (belief degree) real class label sample and further improve accuracy.