Machine learning

Feature selection

Ranking (information retrieval)

Feature (linguistics)

Data mining

Computer science

F1 score

Marketing

Computation

Business

Pattern recognition (psychology)

Algorithm

Class (philosophy)

Linguistics

Philosophy

Artificial intelligence

Selection (genetic algorithm)

Benchmarking

Expert Systems with Applications

F-score is a simple feature selection technique, however, it works only for two classes. This paper proposes a novel feature ranking method based on Fisher discriminate analysis (FDA) and F-score, denoted as FDAF-score, which considers the relative distribution of classes in a multi-dimensional feature space. The main idea is that a proper subset is got according to maximizing the proportion of average between-class distance to the relative within-class scatter. Because the method removes all insignificant features at a time, it can effectively reduce computational cost. Experiments on six benchmarking UCI datasets and two artificial datasets demonstrate that the proposed FDAF-score algorithm can not only obtain good results with fewer features than the original datasets as well as fast computation but also deal with the classification problem with noises well.

Feature selection based on FDA and F-score for multi-class classification