Classifying Words with 3-sort Automata

TL;DR

This paper introduces models for inferring 3-sort non-deterministic finite automata from data, transforming them into probabilistic NFAs, and demonstrates their effectiveness in classification tasks on various datasets.

Contribution

It presents a novel approach for inferring 3-sort NFAs and converting them into probabilistic models for classification, with experimental validation.

Findings

Probabilistic NFAs perform well in classification tasks.

The approach works on real-life and benchmark datasets.

Transformation from 3-sort NFA to probabilistic NFA is effective.

Abstract

Grammatical inference consists in learning a language or a grammar from data. In this paper, we consider a number of models for inferring a non-deterministic finite automaton (NFA) with 3 sorts of states, that must accept some words, and reject some other words from a given sample. We then propose a transformation from this 3-sort NFA into weighted-frequency and probabilistic NFA, and we apply the latter to a classification task. The experimental evaluation of our approach shows that the probabilistic NFAs can be successfully applied for classification tasks on both real-life and superficial benchmark data sets.