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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/52593

Title: One versus one multi-class classification fusion using optimizing decision directed acyclic graph for predicting listing status of companies
Authors: Ligang Zhou
Qingyang Wang
Hamido Fujita
Contributors: 國立臺灣海洋大學:資訊工程學系
Keywords: Multi-class classification
One versus oneListing status
Prediction
Decision directed acyclic graph
Optimizing
Date: 2017-07
Issue Date: 2019-11-22T02:24:03Z
Publisher: Information Fusion
Abstract: Abstract: 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.
Relation: 36 pp.80-89
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/52593
Appears in Collections:[資訊工程學系] 期刊論文

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