# Reference-Based Sequence Classification

## TL;DR

This paper introduces a unified reference-based framework for sequence classification that consolidates existing pattern-based methods and facilitates the development of new algorithms with competitive accuracy.

## Contribution

The paper presents a general framework unifying pattern-based sequence classification methods and enabling the creation of novel algorithms.

## Key findings

- New algorithms achieve comparable accuracy to state-of-the-art methods.
- Framework effectively unifies existing pattern-based approaches.
- Experimental results validate the versatility and effectiveness of the proposed framework.

## Abstract

Sequence classification is an important data mining task in many real world applications. Over the past few decades, many sequence classification methods have been proposed from different aspects. In particular, the pattern-based method is one of the most important and widely studied sequence classification methods in the literature. In this paper, we present a reference-based sequence classification framework, which can unify existing pattern-based sequence classification methods under the same umbrella. More importantly, this framework can be used as a general platform for developing new sequence classification algorithms. By utilizing this framework as a tool, we propose new sequence classification algorithms that are quite different from existing solutions. Experimental results show that new methods developed under the proposed framework are capable of achieving comparable classification accuracy to those state-of-the-art sequence classification algorithms.