Interpretable transformed ANOVA approximation on the example of the prevention of forest fires
This work provides an incremental improvement for researchers in machine learning and environmental science by enhancing interpretability in data analysis for forest fire prevention.
The paper tackled the problem of applying ANOVA approximation to Z-score normalized data by constructing a complete orthonormal system in L2 space with standard normal distribution as weight, enabling interpretable analysis on the forest fires dataset to identify key variables for fire detection.
The distribution of data points is a key component in machine learning. In most cases, one uses min-max normalization to obtain nodes in $[0,1]$ or Z-score normalization for standard normal distributed data. In this paper, we apply transformation ideas in order to design a complete orthonormal system in the $\mathrm{L}_2$ space of functions with the standard normal distribution as integration weight. Subsequently, we are able to apply the explainable ANOVA approximation for this basis and use Z-score transformed data in the method. We demonstrate the applicability of this procedure on the well-known forest fires data set from the UCI machine learning repository. The attribute ranking obtained from the ANOVA approximation provides us with crucial information about which variables in the data set are the most important for the detection of fires.