288 pp. per issue
6 x 9, illustrated
2014 Impact factor:

Neural Computation

July 2009, Vol. 21, No. 7, Pages 2049-2081
(doi: 10.1162/neco.2009.03-08-740)
© 2009 Massachusetts Institute of Technology
A Multiclass Classification Method Based on Decoding of Binary Classifiers
Article PDF (219.95 KB)

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.