Domination dependency analysis of sales marketing based on multi-label classification using label ordering and cycle chain classification

Multi-label classification (MLC) is the technique to solve conditional problem where decisions is a set of labels. Classification is the learning task by using historical examples to make model of conditions to make decision of unseen example. To improve decision in MLC. We can use advantage of domination analysis or dependency relations between the classes by using enhanced Bayesian Chain Classifier (BCC). We introduce an approach for chaining classifier primary order by its individual label accuracy priority (LPC-CC). Our method considers the dependencies among label based on label accuracy priority ordering. Thus, Binary Relevance (BR) theoretical is used for label sequencing priority, and cycle classifiers chain using nave Bayes is for finding domination. The model have been tested on 2 well-known benchmark datasets named Yeast and Emotions and a collection of car sell records from a Thailand Automotive Company.