title: One-class-at-a-time removal sequence planning method for multiclass classification problems.
abstract: Using dynamic programming, this work develops a one-class-at-a-time removal sequence planning method to decompose a multiclass classification problem into a series of two-class problems. Compared with previous decomposition methods, the approach has the following distinct features. First, under the one-class-at-a-time framework, the approach guarantees the optimality of the decomposition. Second, for a K-class problem, the number of binary classifiers required by the method is only K-1. Third, to achieve higher classification accuracy, the approach can easily be adapted to form a committee machine. A drawback of the approach is that its computational burden increases rapidly with the number of classes. To resolve this difficulty, a partial decomposition technique is introduced that reduces the computational cost by generating a suboptimal solution. Experimental results demonstrate that the proposed approach consistently outperforms two conventional decomposition methods.
authors.affiliation: Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan, ROC. opt@cdpa.nsysu.edu.tw
authors.forename: Chieh-Neng
authors.initials: CN
authors.lastname: Young
authors.forename: Chen-Wen
authors.initials: CW
authors.lastname: Yen
authors.forename: Yi-Hua
authors.initials: YH
authors.lastname: Pao
authors.forename: Mark L
authors.initials: ML
authors.lastname: Nagurka
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D016247:Information Storage and Retrieval / Q000379:methods*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted