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Mining Good Sliding Window for Positive Pathogens Prediction in Pathogenic Spectrum Analysis

  • Lei Duan
  • Changjie Tang
  • Chi Gou
  • Min Jiang
  • Jie Zuo
Conference paper
  • 1.1k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7121)

Abstract

Positive pathogens prediction is the basis of pathogenic spectrum analysis, which is a meaningful work in public health. Gene Expression Programming (GEP) can develop the model without predetermined assumptions, so applying GEP to positive pathogens prediction is desirable. However, traditional time-adjacent sliding window may not be suitable for GEP evolving accurate prediction model. The main contributions of this work include: (1) applying GEP-based prediction method to diarrhea syndrome related pathogens prediction, (2) analyzing the disadvantages of traditional time-adjacent sliding window in GEP prediction, (3) proposing a heuristic method to mine good sliding window for generating training set that is used for GEP evolution, (4) proving the problem of training set selection is NP-hard, (5) giving an experimental study on both real-world and simulated data to demonstrate the effectiveness of the proposed method, and discussing some future studies.

Keywords

Data Mining Time Series Sliding Window Pathogens Prediction 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Lei Duan
    • 1
  • Changjie Tang
    • 1
  • Chi Gou
    • 1
  • Min Jiang
    • 2
  • Jie Zuo
    • 1
  1. 1.School of Computer ScienceSichuan UniversityChengduChina
  2. 2.West China School of Public HealthSichuan UniversityChengduChina

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