- 📒 Table of Contents
- 📍 Overview
- 📂 Project Structure
- 🔎 Details of Codes
- 🚀 Getting Started
- 🤝 Collaborators
This project introduces an innovative approach to spam message classification using fuzzy logic. It contains code, datasets, and resources related to the development and evaluation of the spam classification model based on fuzzy logic. Traditional spam filters often use binary classification to determine whether a message is spam or not. This project takes a different route by implementing fuzzy logic, allowing for a more nuanced classification approach. Fuzzy logic takes into account the degree of membership of a message to different classes, which use evolutionary approach to specify the result.
Fuzzy logic is a mathematical approach that deals with uncertainty and imprecision. In the context of spam filtering, fuzzy logic is used to define an evolutionary algorithm that takes into account the "fuzziness" of certain characteristics in messages for the chromosome's fitness when classifying them as spam or not spam.
Before you begin, ensure that you have the packages in requirements.txt installed.
- Clone the CIFAR_10_Image_Classification repository:
git clone https://github.com/kianmajl/Spam_Filtering_Classification.git- Change to the project directory:
cd Spam_Filtering_Classification- Install the dependencies:
pip install -r ./Code/requirements.txtNow you can use the spam filtering classification with an evolutionary method and fuzzy approach as chromosome's fitness to classify SMSs in the dataset.