A technical study and analysis on fuzzy similarity based models for text classification

TL;DR

This paper reviews and compares fuzzy similarity based models for text classification, highlighting their methodologies, effectiveness, and experimental results to advance document categorization techniques.

Contribution

It provides a comprehensive technical review and comparison of fuzzy similarity models for text classification, including experimental analysis and parameter comparisons.

Findings

Fuzzy similarity models effectively categorize text and web documents.

Technical comparisons reveal strengths and limitations of different models.

Experimental results demonstrate the efficiency of fuzzy similarity approaches.

Abstract

In this new and current era of technology, advancements and techniques, efficient and effective text document classification is becoming a challenging and highly required area to capably categorize text documents into mutually exclusive categories. Fuzzy similarity provides a way to find the similarity of features among various documents. In this paper, a technical review on various fuzzy similarity based models is given. These models are discussed and compared to frame out their use and necessity. A tour of different methodologies is provided which is based upon fuzzy similarity related concerns. It shows that how text and web documents are categorized efficiently into different categories. Various experimental results of these models are also discussed. The technical comparisons among each model's parameters are shown in the form of a 3-D chart. Such study and technical review provide a strong base of research work done on fuzzy similarity based text document categorization.