Arabic documents classification using fuzzy R.B.F. classifier with sliding window

TL;DR

This paper introduces an improved fuzzy radial basis function classifier with a sliding window for more accurate Arabic document classification, leveraging semantic neighborhood promotion and kernel-based similarity measures.

Contribution

It enhances the fuzzy model with radial basis functions and sliding window techniques to better capture semantic context in Arabic document classification.

Findings

Achieved high classification accuracy on Arabic press dataset

Outperformed existing methods in literature

Demonstrated effectiveness of semantic neighborhood promotion

Abstract

In this paper, we propose a system for contextual and semantic Arabic documents classification by improving the standard fuzzy model. Indeed, promoting neighborhood semantic terms that seems absent in this model by using a radial basis modeling. In order to identify the relevant documents to the query. This approach calculates the similarity between related terms by determining the relevance of each relative to documents (NEAR operator), based on a kernel function. The use of sliding window improves the process of classification. The results obtained on a arabic dataset of press show very good performance compared with the literature.