One versus one multi-class classification fusion using optimizing decision directed acyclic graph for predicting listing status of companies

Most existing research has demonstrated the success of different decomposition and ensemble strategies for solving multi-class classification problems. This study proposes a new ensemble strategy for One-vs-One (OVO) scheme that uses optimizing decision directed acyclic graph (ODDAG) whose structure is determined by maximizing the fitness on the training set instead of by predefined rules. It makes an attempt to reduce the effect of non-competent classifiers in OVO scheme like decision directed acyclic graph (DDAG) but in another way. We test the proposed method on some public data sets and compare it to some other widely used methods to select the proper candidates and related settings for a problem with practical concern from financial industry in China, i.e. the prediction of listing status of companies. The experimental result shows that our model can outperform the benchmarked methods on this real problem. In addition, the ODDAG combined with decision tree is a white box model whose internal rules can be viewed and checked by decision makers.