Journal of Agricultural Extension & Community Development (농촌지도와개발)

Korean Society of Agricultural Extension (한국농촌지도학회)
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Forecasting Sow's Productivity using the Machine Learning Models

머신러닝을 활용한 모돈의 생산성 예측모델

  • Received : 2009.10.30
  • Accepted : 2009.12.18
  • Published : 2009.12.30
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Abstract

The Machine Learning has been identified as a promising approach to knowledge-based system development. This study aims to examine the ability of machine learning techniques for farmer's decision making and to develop the reference model for using pig farm data. We compared five machine learning techniques: logistic regression, decision tree, artificial neural network, k-nearest neighbor, and ensemble. All models are well performed to predict the sow's productivity in all parity, showing over 87.6% predictability. The model predictability of total litter size are highest at 91.3% in third parity and decreasing as parity increases. The ensemble is well performed to predict the sow's productivity. The neural network and logistic regression is excellent classifier for all parity. The decision tree and the k-nearest neighbor was not good classifier for all parity. Performance of models varies over models used, showing up to 104% difference in lift values. Artificial Neural network and ensemble models have resulted in highest lift values implying best performance among models.

Keywords

References

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