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.
This work was supported by the National Research Foundation for the Doctoral Program by the Chinese Ministry of Education under grant No.20100181120029, and the Young Faculty Foundation of Sichuan University under grant No. 2009SCU11030.
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Duan, L., Tang, C., Gou, C., Jiang, M., Zuo, J. (2011). Mining Good Sliding Window for Positive Pathogens Prediction in Pathogenic Spectrum Analysis. In: Tang, J., King, I., Chen, L., Wang, J. (eds) Advanced Data Mining and Applications. ADMA 2011. Lecture Notes in Computer Science(), vol 7121. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25856-5_12
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