Class-Level Feature Selection Method Using Feature Weighted Growing Self-Organising Maps
This addresses the problem of improving predictive accuracy and interpretability in datasets with class-specific features for researchers and practitioners in machine learning, though it is incremental as it builds on existing self-organising map techniques.
The paper tackles the limitation of global feature selection algorithms by developing a class-level feature selection method called FWGSOM, which identifies features relevant to specific classes, resulting in better performance, explainable class-level results, and lower computational cost compared to other methods.
There have been several attempts to develop Feature Selection (FS) algorithms capable of identifying features that are relevant in a dataset. Although in certain applications the FS algorithms can be seen to be successful, they have similar basic limitations. In all cases, the global feature selection algorithms seek to select features that are relevant and common to all classes of the dataset. This is a major limitation since there could be features that are specifically useful for a particular class while irrelevant for other classes, and full explanation of the relationship at class level therefore cannot be determined. While the inclusion of such features for all classes could cause improved predictive ability for the relevant class, the same features could be problematic for other classes. In this paper, we examine this issue and also develop a class-level feature selection method called the Feature Weighted Growing Self-Organising Map (FWGSOM). The proposed method carries out feature analysis at class level which enhances its ability to identify relevant features for each class. Results from experiments indicate that our method performs better than other methods, gives explainable results at class level, and has a low computational footprint when compared to other methods.