Incremental Learning Probabilistic State Machine for Symbolic Sequences Classification

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jittakorn pullpothong

Abstract

Symbolic sequence classification can be used in a variety of applications such as DNA sequences analysis, intrusion detection, electrocardiography (ECG) analysis. The convention methods can be applied to this field for example, probabilistic language model, support vector machine, and artificial neural network. However, learning unknown words in very long sequences such as DNA sequences need fix size of imitation words. Consequently, the probability and position of original words are distorted which can lead to incorrect results in long term. Moreover, learning update process may cause confliction with the former fix size knowledge. In this work, to optimise the probability and the position of original words as possible while the direction of data is still tractable, we propose a novel probabilistic language model using Markov assumption with variable memory length considered by unique and repeated substring. We propose an on-line algorithm to build the model with time and space complexity . The experiment of classification applied to DNA sequences of promoter and non-promoter bacteria E. Coli. The error of classification is only 3.77 % which is satisfied compared to the other methods.


 

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How to Cite
pullpothong, jittakorn. (2021). Incremental Learning Probabilistic State Machine for Symbolic Sequences Classification. Ramkhamhaeng Research Journal of Sciences and Technology, 24(1), 1–13. Retrieved from https://li01.tci-thaijo.org/index.php/rusci/article/view/230700
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Author Biography

jittakorn pullpothong, Ramkhamhaeng University

ผู้ช่วยศาสตราจารย์ สาขาวิศวกรรมคอมพิวเตอร์ คณะวิศวกรรมศาสตร์ มหาวิทยาลัยรามคำแหง

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