Mining good sliding window for positive pathogens prediction in pathogenic spectrum analysis

  • Authors:

  • Lei Duan;Changjie Tang;Chi Gou;Min Jiang;Jie Zuo

  • Affiliations:

  • School of Computer Science, Sichuan University, Chengdu, China;School of Computer Science, Sichuan University, Chengdu, China;School of Computer Science, Sichuan University, Chengdu, China;West China School of Public Health, Sichuan University, Chengdu, China;School of Computer Science, Sichuan University, Chengdu, China

  • Venue:
  • ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
  • Year:
  • 2011

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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.