title: A multiclass classification method based on decoding of binary classifiers.
abstract: 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.
authors.affiliation: Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma, Nara 630-0192, Japan. ttakashi@is.naist.jp
authors.forename: Takashi
authors.initials: T
authors.lastname: Takenouchi
authors.forename: Shin
authors.initials: S
authors.lastname: Ishii
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011237:Predictive Value of Tests