What are the most effective feature selection and extraction techniques for ANN?

1 Filter methods

Filter methods are the simplest and fastest way to select features for ANN. They use statistical tests or measures to rank the features according to their relevance or correlation with the target variable, and then select a subset of features that meet a certain threshold or criterion. Filter methods are independent of the ANN model, and can be applied before training. Some examples of filter methods are chi-square test, information gain, and variance threshold. Filter methods are useful for removing noisy or irrelevant features, but they may also discard some potentially useful features that interact with other features or have nonlinear relationships with the target.

2 Wrapper methods

Wrapper methods are more sophisticated and flexible than filter methods, but also more computationally expensive and prone to overfitting. They use the ANN model itself as a black box to evaluate the performance of different subsets of features, and then select the optimal subset that maximizes a predefined objective function, such as accuracy or F1-score. Wrapper methods are dependent on the ANN model, and can be applied during training. Some examples of wrapper methods are forward selection, backward elimination, and recursive feature elimination. Wrapper methods are useful for finding the best combination of features for a specific ANN model, but they may also introduce bias or variance due to the high number of evaluations and iterations.

3 Embedded methods

Embedded methods are a hybrid of filter and wrapper methods, that combine the advantages of both while avoiding some of their drawbacks. They use the ANN model to learn the importance or weight of each feature, and then select a subset of features that have high values or satisfy a certain condition. Embedded methods are also dependent on the ANN model, but they are more efficient and robust than wrapper methods, as they require fewer evaluations and iterations. Some examples of embedded methods are L1 regularization, L2 regularization, and dropout. Embedded methods are useful for reducing the complexity and improving the generalization of the ANN model, but they may also lose some interpretability or stability due to the regularization or randomization techniques.

4 Transformation methods

Transformation methods are another way to extract features for ANN, that do not select a subset of features, but rather create new features from the original ones. They use mathematical or statistical operations to transform the features into a lower-dimensional or higher-dimensional space, where they may capture more information or reveal more patterns. Transformation methods are independent of the ANN model, and can be applied before or after training. Some examples of transformation methods are principal component analysis, linear discriminant analysis, and autoencoders. Transformation methods are useful for enhancing or compressing the features, but they may also alter or lose some original information or meaning due to the transformation.

5 Hybrid methods

Hybrid methods are the most advanced and comprehensive way to select and extract features for ANN, that combine two or more of the previous methods to achieve better results. They use a sequential or parallel approach to apply different methods at different stages or levels, and then integrate the outcomes into a final subset or set of features. Hybrid methods are dependent on the ANN model and the methods involved, and can be applied before, during, or after training. Some examples of hybrid methods are genetic algorithms, deep belief networks, and convolutional neural networks. Hybrid methods are useful for exploring or exploiting the features, but they may also require more time or resources due to the complexity and diversity of the methods.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?