Multi-objective evolutionary optimization for dimensionality reduction of texts represented by synsets.

ABSTRACT: Despite new developments in machine learning classification techniques, improving the accuracy of spam filtering is a difficult task due to linguistic phenomena that limit its effectiveness. In particular, we highlight polysemy, synonymy, the usage of hypernyms/hyponyms, and the presence of irrelevant/confusing words. These problems should be solved at the pre-processing stage to avoid using inconsistent information in the building of classification models. Previous studies have suggested that the use of synset-based representation strategies could be successfully used to solve synonymy and polysemy problems. Complementarily, it is possible to take advantage of hyponymy/hypernymy-based to implement dimensionality reduction strategies. These strategies could unify textual terms to model the

SUBMITTER: Velez de Mendizabal I 

PROVIDER: S-EPMC10280406 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature