Nested Cavity Classifier: performance and remedy
This work provides a remedy for a geometric-based classifier's inefficiency, which is incremental and relevant for researchers in classification methods.
The paper addresses inefficiencies in the Nested Cavity Classifier (NCC) by proposing a hybrid method, Nested Cavity Discriminant Analysis (NCDA), which combines NCC with Linear Discriminant Analysis (LDA). In simulations, NCC alone performed worse than basic classifiers, while NCDA outperformed NCC and was competitive with LDA and Quadratic Discriminant Analysis (QDA).
Nested Cavity Classifier (NCC) is a classification rule that pursues partitioning the feature space, in parallel coordinates, into convex hulls to build decision regions. It is claimed in some literatures that this geometric-based classifier is superior to many others, particularly in higher dimensions. First, we give an example on how NCC can be inefficient, then motivate a remedy by combining the NCC with the Linear Discriminant Analysis (LDA) classifier. We coin the term Nested Cavity Discriminant Analysis (NCDA) for the resulting classifier. Second, a simulation study is conducted to compare both, NCC and NCDA to another two basic classifiers, Linear and Quadratic Discriminant Analysis. NCC alone proves to be inferior to others, while NCDA always outperforms NCC and competes with LDA and QDA.