Learning from hints in neural networks
Journal of Complexity
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Principle of information diffusion
Fuzzy Sets and Systems
Virtual sample generation using a population of networks
Neural Processing Letters
Grey Information: Theory and Practical Applications (Advanced Information and Knowledge Processing)
Grey Information: Theory and Practical Applications (Advanced Information and Knowledge Processing)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Utilize bootstrap in small data set learning for pilot run modeling of manufacturing systems
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
An improved grey-based approach for early manufacturing data forecasting
Computers and Industrial Engineering
Effective feature selection scheme using mutual information
Neurocomputing
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In the current highly competitive manufacturing environment, it is important to have effective and efficient control of manufacturing systems to obtain and maintain competitive advantages. However, developing appropriate forecasting models for such systems can be challenging in their early stages, as the sample sizes are usually very small, and thus there is limited data available for analysis. The technique of virtual sample generation is one way to address this issue, but this method is usually not directly applied to time series data. This research thus develops a Latent Information function to analyze data features and extract hidden information, in order to learn from small data sets considering timing factors. The experimental results obtained using the Synthetic Control Chart Time Series and aluminum price datasets show that the proposed method can significantly improve forecasting accuracy, and thus is considered an appropriate procedure to forecast manufacturing outputs based on small samples.