One-Class-at-a-Time Removal Sequence Planning Method for Multiclass Classification Problems

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...

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Bibliographic Details
Published inIEEE transactions on neural networks Vol. 17; no. 6; pp. 1544 - 1549
Main Authors Chieh-Neng Young, Chen-Wen Yen, Yi-Hua Pao, Nagurka, M.L.
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.11.2006
Institute of Electrical and Electronics Engineers
Subjects
Algorithms
Applied sciences
Artificial Intelligence
Classification
Classifiers
Cluster Analysis
Computational efficiency
Computer science; control theory; systems
Connectionism. Neural networks
Councils
Decomposition
Design methodology
Dynamic programming
Exact sciences and technology
Industrial engineering
Information Storage and Retrieval - methods
Mathematical models
multiclass classification
Neural networks
Neural Networks (Computer)
Optimization
Pattern recognition
Pattern Recognition, Automated - methods
Series (mathematics)
Signal Processing, Computer-Assisted
Voting
multiclass classification
Classification
Planning
Dynamic programming
Pattern recognition
Neural network
Binary number
Multiclass
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Summary: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
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ISSN:1045-9227
1941-0093
DOI:10.1109/TNN.2006.879768