Optimizing Ensemble Weights for Machine Learning Models: A Case Study for Housing Price Prediction

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2019-01-01
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Shahhosseini, Mohsen
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Hu, Guiping
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Industrial and Manufacturing Systems Engineering
The Department of Industrial and Manufacturing Systems Engineering teaches the design, analysis, and improvement of the systems and processes in manufacturing, consulting, and service industries by application of the principles of engineering. The Department of General Engineering was formed in 1929. In 1956 its name changed to Department of Industrial Engineering. In 1989 its name changed to the Department of Industrial and Manufacturing Systems Engineering.
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Designing ensemble learners has been recognized as one of the significant trends in the field of data knowledge especially in data science competitions. Building models that are able to outperform all individual models in terms of bias, which is the error due to the difference in the average model predictions and actual values, and variance, which is the variability of model predictions, has been the main goal of the studies in this area. An optimization model has been proposed in this paper to design ensembles that try to minimize bias and variance of predictions. Focusing on service sciences, two well-known housing datasets have been selected as case studies: Boston housing and Ames housing. The results demonstrate that our designed ensembles can be very competitive in predicting the house prices in both Boston and Ames datasets.

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This is a manuscript of the conference proceeding Shahhosseini, Mohsen, Guiping Hu, and Hieu Pham. "Optimizing Ensemble Weights for Machine Learning Models: A Case Study for Housing Price Prediction." 2019 INFORMS Conference on Service Science (CSS2019). Posted with permission.

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Tue Jan 01 00:00:00 UTC 2019