/Feature-Optimization-using-Genetic-Algorithm

A predictive Analysis System for Feature Optimization using Genetic Algorithm and 10-fold cross validation method.

Primary LanguagePythonCreative Commons Attribution Share Alike 4.0 InternationalCC-BY-SA-4.0


A predictive Analysis System for Feature Optimization using Genetic Algorithm


How to run code

1. Clone the repository

https://github.com/hbkabir004/Feature-Optimization-using-Genetic-Algorithm.git

2. Install Python

2.1 Download the Python Installer binaries.

https://www.python.org/downloads/

2.2 Run the Executable Installer.

2.3 Add Python to environmental variables.

2.4 Verify the Python Installation.


3. Install & Import Necessary Python Packages in Visual Studio Code

Follow the tutorial to install & import necessary python packages in VS Code

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4. Open the project in the IDE (VS CODE recommended)

Follow the tutorial to Run Python in Visual Studio Code on Windows 10

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5. RESULTs

5.1 RANDOM FOREST

Optimal Feature Set

['radius_mean', 'texture_mean', 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean', 'concave points_mean', 'symmetry_mean', 'radius_se', 'perimeter_se', 'concavity_se', 'radius_worst', 'texture_worst', 'area_worst', 'concave points_worst', 'fractal_dimension_worst']

Feature Importances

[0.0345544  0.01528677 0.05257772 0.04909357 0.00796349 0.0073864
 0.03402101 0.0742245  0.00459763 0.00389691 0.00996118 0.00515219
 0.01823021 0.04250951 0.00312534 0.00529447 0.00446035 0.00320924
 0.00378835 0.00521066 0.14585278 0.02144762 0.14566475 0.09814828
 0.01441097 0.01654489 0.04011699 0.1168934  0.01104655 0.00532988]
Optimal Accuracy = 99 %

Average Accuracy saved  0.961335676625659
Average Precision       0.9612987777153709
Average Recall          0.9557766502827546
Average F1-Score        0.9584064327485381

     B    M
B  349    8
M   14  198

5.2 Light GBM

Optimal Feature Set

 ['smoothness_mean', 'concavity_mean', 'concave points_mean', 'perimeter_se', 'area_se', 'concave points_se', 'radius_worst', 'texture_worst', 'perimeter_worst', 'area_worst', 'smoothness_worst', 'compactness_worst', 'concavity_worst', 'concave points_worst', 'symmetry_worst', 'fractal_dimension_worst']

Feature Importances

[ 69 170  49  36  65  64  88 174  81  54  80  79  48 142  54  43  27  47
  57  47  92 282 147 144 104  40 109 206 107  42]
Optimal Accuracy = 99 %

Average Accuracy saved  0.9718804920913884
Average Precision       0.9707985143918292
Average Recall          0.9689696633370328
Average F1-Score        0.9698694696708942

     B      M
B   350     7
M   9     203

5.3 XGBoost

Optimal Feature Set

['perimeter_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean', 'concave points_mean', 'fractal_dimension_mean', 'concavity_se', 'concave points_se', 'symmetry_se', 'fractal_dimension_se', 'texture_worst', 'area_worst', 'compactness_worst', 'concave points_worst', 'symmetry_worst']

Feature Importances

[0.00523759 0.01394322 0. 0.01937084 0.00388636 0.00419483
0.00792941 0.12601759 0.00246526 0.00336443 0.00851927 0.01265129
0.00763103 0.00893879 0.00833281 0.0060065 0.01219247 0.01192982
0.00208353 0.00228187 0.3791942 0.0181876 0.19956343 0.01817427
0.00790597 0.00302455 0.01822192 0.07777867 0.00269732 0.0082751 ]
Optimal Accuracy = 99 %

Average Accuracy saved  0.9648506151142355
Average Precision       0.966116035455278
Average Recall          0.9585777707309339
Average F1-Score        0.9621111229490731

     B      M
B   351     6
M   14    198