Minimizing State Transition Model for Multiclassification by Mixed-Integer Programming

Abstract

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.