Subspace-based dynamic selection for high-dimensional data - Maciel-Guerra, Alexandre (2022)
The number of features collected has increased greatly in the past decade, particularly in medicine and life sciences, which brings challenges and opportunities. Making reliable predictions, exploring associations and extracting meaningful information in high-dimensional data are some of the problems that are yet to be solved. Due to intrinsic properties of high-dimensional spaces such as distance concentration and hubness, traditional classification and clustering algorithms face difficult challenges. In general, a Multiple Classifier System (MCS) provides better classification accuracy than individual classifiers. One of the most promising approaches to MCS is Dynamic Selection (DS) methods, which work by selecting classifiers on the fly, according to each unknown test sample. The rationale behind this is that not every classifier is an expert in predicting all samples, rather each classifier or a combination of classifiers is an expert in a different region of the feature space; whose quality can significantly impact the overall performance.