Lattice-cell : Hybrid approach for text categorization
This work addresses text categorization efficiency, but it appears incremental as it builds on existing lattice and automata methods without a major breakthrough.
The authors tackled the problem of slow text categorization by proposing a hybrid framework combining Concept Lattice and cellular automata, which improved performance and reduced categorization time compared to methods like Naive Bayes and k-nearest neighbors.
In this paper, we propose a new text categorization framework based on Concepts Lattice and cellular automata. In this framework, concept structure are modeled by a Cellular Automaton for Symbolic Induction (CASI). Our objective is to reduce time categorization caused by the Concept Lattice. We examine, by experiments the performance of the proposed approach and compare it with other algorithms such as Naive Bayes and k nearest neighbors. The results show performance improvement while reducing time categorization.