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