Clashing with Mirror Images
In the realm of industrial machine learning, reducing feature redundancy is crucial for improving model performance and navigating complex sensor data. A strategy for analyzing and pruning a network of linear correlations in large datasets has been presented in a recent article. This approach aims to identify and remove redundant or highly correlated variables that do not bring additional information.
The strategy offers two primary approaches: keeping the most central variables (centrality criterion) or keeping the most peripheral variables (peripherality criterion). The doppelganger R package simplifies the pruning process, allowing it to be performed in a single line of code.
The centrality criterion involves scanning variables in decreasing order of centrality degree, calculated as the mean of absolute values of the correlations. The peripherality criterion, on the other hand, scans variables in increasing order of centrality degree. In both cases, a threshold is set for correlation to determine whether a correlation is relevant or not.
The article provides an example of a more complex network correlation obtained from seven simulated variables. The example shows that, using the centrality criterion, the variables gamma, epsilon, and zeta are retained after pruning. However, it's important to note that the choice of which variables to drop can be non-unique, with different approaches leading to different results.
The pruning strategy aims to drop the most possible number of variables while retaining the greatest possible amount of information. It starts by keeping the first variable in the ranking and dropping its doppelgängers.
In addition to the centrality and peripherality criteria, the linear correlation index serves as a tool to identify duplicated or nearly-duplicated variables. The pruning of a pool of candidate predictors can be an effective ally in building a light and efficient model.
Visualizing correlations using heatmaps or similar tools is also essential in identifying clusters of highly correlated features. Defining a correlation threshold (such as >0.8 or >0.9) helps determine when features are considered highly correlated and candidates for pruning.
For pairs or groups of highly correlated variables, one representative feature is selected based on its importance (e.g., correlation with the target variable, domain knowledge, or feature importance metrics). Removing the redundant ones decreases dimensionality, simplifies the model, and improves interpretability.
Instead of dropping features, dimensionality reduction techniques like Principal Component Analysis (PCA) can be used to create composite features capturing most variance without redundancy.
In industrial machine learning applications, reducing feature redundancy is critical due to complex sensor data and multivariate measurements prone to collinearity. Pruning reduces model complexity, improves generalization, and reduces computational load.
Additional practical advice includes checking correlation not just between features but also between features and the target variable to prioritize predictive value, performing feature pruning iteratively, and validating model performance after each step to avoid removing useful information. Coupling correlation-based pruning with other preprocessing (like handling missing values or eliminating duplicates) also enhances data quality.
In conclusion, careful correlation analysis followed by strategic pruning or transformation of correlated variables effectively reduces redundancy and enhances model performance in industrial machine learning tasks. When the correlation between two variables is approximately 1, retaining one or the other is statistically irrelevant. The strategy suggests ranking variables according to their centrality degree, with the most central nodes being the most representative variables of the network.
- The pruning strategy in data-and-cloud-computing, as applied to industrial machine learning, leverages science and technology by pinpointing and removing redundant or highly correlated variables, which helps create light and efficient models.
- In the process of pruning a network of linear correlations in large datasets, one can utilize the doppelganger package, a technology tool that simplifies the pruning process, aiding in reducing feature redundancy.
