This paper proposes a state transition (ST) model as a classifier and its generalization by the minimization. Different from previous works using statistical methods, tree-based classifiers and neural networks, we use a ST model which determines classes of strings. Though an initial ST model only accepts given strings, the minimum ST model can accepts various strings by the generalization. We use a minimization algorithm by Mixed-Integer Linear Programming (MILP) approach. The MILP approach guarantees a minimum solution. Experiment was done for the classification of pseudo-strings. Experimental results showed that the reduction ratio from an initial ST model to the minimal ST model becomes small, as the number of examples increases. However, a current MILP solver was not feasible for large scale ST models in our formalization. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Inui, N., & Shinano, Y. (2005). Minimizing state transition model for multiclassification by mixed-integer programming. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3789 LNAI, pp. 473–482). https://doi.org/10.1007/11579427_48
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