Feature Selection Genetic Algorithm and Classification

This repository contains Python code for feature selection using a genetic algorithm and various classification algorithms applied to the well-known WDBC dataset (Wisconsin Diagnosis Breast Cancer dataset). The dataset is loaded from a CSV file using pandas, and preprocessing steps include handling missing values and label encoding.

Feature Selection with Genetic Algorithm

The genetic algorithm is implemented using the DEAP library to evolve a population of binary strings representing selected features.

Classification Algorithms

The selected features are used to train SVM, Random Forest, Decision Tree, and Artificial Neural Network (ANN) classifiers. Additionally, a Multi-Layer Perceptron (MLP) classifier is included for comparison.

Evaluation Metrics

The accuracy, precision, recall, and F1-score are calculated for each classifier. Confusion matrices are generated for the ANN classifier, and precision-recall metrics are computed for the MLP classifier.

Dependencies

The code utilizes popular Python libraries such as NumPy, scikit-learn, pandas, Matplotlib, Seaborn, DEAP, Keras, and others. The data can be loaded either locally or from Google Colab.

How to Use

1. Environment Setup

   • Make sure you have access to Google Colab.
   • Upload the dataset (data.csv) to your Google Colab environment.

2. Install Dependencies

   • Open a new Colab notebook.
   • Install the required libraries in requirements.txt file

3. Run the Code

   • Copy the provided code into separate cell (or cells) in your Colab notebook.

4. Adjust Parameters (Optional)

   • You can customize parameters such as population size, generations, crossover probability, and mutation probability to suit your preferences.

5. Review Results

   • After running the code, the results, including classifier accuracies and evaluation metrics, will be printed in the Colab output.

6. Explore and Experiment

   • Feel free to experiment with different datasets, tweak the genetic algorithm parameters, or try other classifiers to see how the system behaves.

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Takes into account that Google Colab provides a pre-installed environment for many popular libraries, and you only need to install additional dependencies listed in the requirements.txt file.

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