Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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A Implementation of Optimal Multiple Classification System using Data Mining for Genome Analysis

  • Received : 2018.09.07
  • Accepted : 2018.12.12
  • Published : 2018.12.31
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Abstract

In this paper, more efficient classification result could be obtained by applying the combination of the Hidden Markov Model and SVM Model to HMSV algorithm gene expression data which simulated the stochastic flow of gene data and clustering it. In this paper, we verified the HMSV algorithm that combines independently learned algorithms. To prove that this paper is superior to other papers, we tested the sensitivity and specificity of the most commonly used classification criteria. As a result, the K-means is 71% and the SOM is 68%. The proposed HMSV algorithm is 85%. These results are stable and high. It can be seen that this is better classified than using a general classification algorithm. The algorithm proposed in this paper is a stochastic modeling of the generation process of the characteristics included in the signal, and a good recognition rate can be obtained with a small amount of calculation, so it will be useful to study the relationship with diseases by showing fast and effective performance improvement with an algorithm that clusters nodes by simulating the stochastic flow of Gene Data through data mining of BigData.

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CPTSCQ_2018_v23n12_43_f0001.png 이미지

Fig. 1. The execution results of the HMSV algorithm proposedin this paper

CPTSCQ_2018_v23n12_43_f0002.png 이미지

Fig. 2. Sensitivity and specification of K-means, SOM, HMSV

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