SEM-ANN uses SEM to do feature selection before using ANN and let ANN is easy to be interpreted finally.
Another way is using all features then use shapley value to interpret.
What are the differences in underlying logic&using scenario between them?
SEM-ANN and ANN-Shapley are two different approaches for combining Structural Equation Modeling (SEM) and Artificial Neural Networks (ANNs) to analyze complex data.
SEM-ANN combines SEM and ANNs by using the latent variables generated from SEM as inputs to the ANN. This approach is useful when there is a priori knowledge of the causal relationships among variables, and the latent variables can be used to reduce the dimensionality of the data. SEM-ANN is typically used for prediction tasks, where the ANN is trained to predict an outcome variable based on the input variables generated from SEM.
On the other hand, ANN-Shapley is a method that combines ANNs and the Shapley value, a game theory concept that measures the contribution of each input variable to the output of the ANN. The Shapley value provides a way to attribute the contribution of each input variable to the overall prediction of the ANN, which can be useful for understanding the importance of different features in the data. ANN-Shapley is typically used for feature selection and interpretation tasks, where the goal is to identify the most important features for predicting the outcome variable.
In summary, SEM-ANN is a method that combines SEM and ANNs for prediction tasks, while ANN-Shapley is a method that combines ANNs and the Shapley value for feature selection and interpretation tasks.
SEM-ANN and ANN-Shapley are two different approaches used for combining structural equation modeling (SEM) and artificial neural networks (ANN) to analyze complex data.
SEM-ANN is a hybrid method that combines the strengths of SEM and ANN to capture the complex relationships among the variables. In SEM-ANN, the SEM is used to model the structural relationships among the latent variables, while the ANN is used to model the relationships between the observed variables and the latent variables. The SEM-ANN approach can be used for both exploratory and confirmatory purposes and has been shown to perform well in handling non-linear relationships and complex data structures.
On the other hand, ANN-Shapley is a method used to identify the relative importance of the input variables in an ANN model. The Shapley value is a mathematical concept that assigns a value to each input variable based on its contribution to the prediction of the output variable. The ANN-Shapley approach uses this concept to identify the relative importance of the input variables in the ANN model and can be used for feature selection and interpretation.
Therefore, the key difference between SEM-ANN and ANN-Shapley is that SEM-ANN combines SEM and ANN for modeling complex relationships, while ANN-Shapley focuses on identifying the relative importance of the input variables in an ANN model.
In summary, SEM-ANN is a hybrid approach that combines SEM and ANN to model complex relationships, while ANN-Shapley is a method used to identify the relative importance of the input variables in an ANN model. Both methods can be used in different contexts to analyze complex data, but they have different objectives and use different techniques.
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