2009年7月
A Multiclass Classification Method Based on Decoding of Binary Classifiers
Neural Computation
- ,
- 巻
- 21
- 号
- 7
- 開始ページ
- 2049
- 終了ページ
- 2081
- 記述言語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1162/neco.2009.03-08-740
- 出版者・発行元
- MIT Press - Journals
In this letter, we present new methods of multiclass classification that combine multiple binary classifiers. Misclassification of each binary classifier is formulated as a bit inversion error with probabilistic models by making an analogy to the context of information transmission theory. Dependence between binary classifiers is incorporated into our model, which makes a decoder a type of Boltzmann machine. We performed experimental studies using a synthetic data set, data sets from the UCI repository, and bioinformatics data sets, and the results show that the proposed methods are superior to the existing multiclass classification methods.
- リンク情報
- ID情報
-
- DOI : 10.1162/neco.2009.03-08-740
- ISSN : 0899-7667
- eISSN : 1530-888X
- Web of Science ID : WOS:000266959100009