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Publication
Survey data preprocessing for optimal modelling through ANNs applied to management environments
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| 1.15 MB | Adobe PDF |
Advisor(s)
Abstract(s)
Surveys are one of the most important tasks to be executed to get valued information. One of the main problems is how the data about many different persons can be processed to give good information about their environment. Modelling environments through Artificial Neural Networks (ANNs) is highly common because ANN’s are excellent to model predictable environments using a set of data. ANN’s are good in dealing with sets of data with some noise, but they are fundamentally surjective mathematical functions, and they aren’t able to give different results for the same input. So, if an ANN is trained using data where samples with the same input configuration has different outputs, which can be the case of survey data, it can be a major problem for the success of modelling the environment. The environment used to demonstrate the study is a strategic environment that is used to predict the impact of the applied strategies to an organization financial result, but the conclusions are not limited to this type of environment. Therefore, is necessary to adjust, eliminate invalid and inconsistent data. This permits one to maximize the probability of success and precision in modeling the desired environment. This study demonstrates, describes and evaluates each step of a process to prepare data for use, to improve the performance and precision of the ANNs used to obtain the model. This is, to improve the model quality. As a result of the studied process, it is possible to see a significant improvement both in the possibility of building a model as in its accuracy.
Description
Keywords
Survey Data Processing Modelling Neural networks ANN
Citation
Texeira-Quirós, J., Justino, M.R.T.F., Gonçalves, A.J., Antunes, M.G., Mucharreira, P.R. (2024). Survey data preprocessing for optimal modelling through ANNs applied to management environments. Journal of Infrastructure, Policy and Development, 8(9), 7108. https://doi.org/10.24294/jipd.v8i9.7108
Publisher
EnPress Publisher