Benchmarking Analysis of the Accuracy of Classification Methods Related to Entropy.

ABSTRACT: In the machine learning literature we can find numerous methods to solve classification problems. We propose two new performance measures to analyze such methods. These measures are defined by using the concept of proportional reduction of classification error with respect to three benchmark classifiers, the random and two intuitive classifiers which are based on how a non-expert person could realize classification simply by applying a frequentist approach. We show that these three simple methods are closely related to different aspects of the entropy of the dataset. Therefore, these measures account somewhat for entropy in the dataset when evaluating the performance of classifiers. This allows us to measure the improvement in the classification results compared to simple methods, and at t

SUBMITTER: Orenes Y 

PROVIDER: S-EPMC8306704 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature