relevant features and discarding the irrelevant and redundant ones” Note: not talking about feature extraction for dimensionality reduction! PCA, t-SNE, manifold learning? No, they lose the meaning of original features
and increase algorithm speed Feature set reduction To save resources in the next round of data collection Performance improvement To gain in predictive accuracy Data understanding To gain knowledge about the process that generated the data or for visualization
Bolón-Canedo, Verónica, et al. "Exploring the consequences of distributed feature selection in DNA microarray data." In Proceedings of International Joint Conference on Neural Networks, IJCNN, pp. 1665-1672, (2017).
not flexible with respect to input features Find flexible feature selection methods capable of modifying the selected subset of features as new training samples arrive Methods that can be executed in a dynamic feature space initially empty but would add features as new information arrives
A model is only as good as its features, so features play a preponderant role in model interpretability Two-fold need for interpretability and transparency in feature selection and model creation processes: • More interactive model visualizations to better interact with the model and visualize future scenarios • More interactive feature selection process where, using interactive visualizations, it is possible to iterate through different feature subsets
data in a meaningful way Data-rich/Knowledge-poor Data-rich/Knowledge-rich Krause, J., Perer, A., & Bertini, E. (2014). INFUSE: interactive feature selection for predictive modeling of high dimensional data. IEEE transactions on visualization and computer graphics, 20(12), 1614-1623.