Abstract
Despite the wide variety of algorithms that exist to build predictive models, it can still be difficult to make accurate predictions for unknown values for certain types of data. New and innovative techniques are needed to overcome the problems underlying these difficulties for poor quality data, or data with a lack of available training cases. In this paper the authors propose a technique for integrating data from related datasets with the aim of improving the accuracy of predictions using Artificial Neural Networks. An overall improvement in the prediction power of models was shown when using the integration algorithm, when compared to models constructed using non-integrated data.
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Trundle, P., Neagu, D., Craciun, M., Chaudhry, Q. (2007). Development of Multi-output Neural Networks for Data Integration — A Case Study. In: Corchado, E., Corchado, J.M., Abraham, A. (eds) Innovations in Hybrid Intelligent Systems. Advances in Soft Computing, vol 44. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74972-1_12
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DOI: https://doi.org/10.1007/978-3-540-74972-1_12
Publisher Name: Springer, Berlin, Heidelberg
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