SALP SWARM ALGORITHM BASED FEATURE SELECTION AND CLASSIFICATION-PYTHON PROGRAMMING
DESIGN DETAILS
The objective of this work is to design and implement the binary salp swarm algorithm to address the feature selection optimization problems by developing a python-based framework. The feature selection is a preprocessing stage applied to a dataset for identifying the near-optimal set of features from the original feature superset. The generated reduced dataset may produce better performance compared with the complete set of features.
APPROACH
Implement a binary version of Salp Swarm Algorithm to address the feature selection problem.
K-Nearest Neighbor Classifier will be used as the fitness function to maximize the classification accuracy.
EVALUATION METRICS
Classification accuracy
Fitness value results can be shown for every iteration.
Execution time
Precision, Recall, F-Score
History of selected features and number of features used during the iterations.
DEVELOPMENT PLATFORM
Ubuntu 18.04/Windows 10
Python 3.6 & above
Libraries: Scikit-learn, SciPy, Pandas/Dask, Numpy, Matplotlib
Coding Environment: PyCharm IDE
JupyterLab for interactive programming
DATASET
Breast cancer - can be downloaded from the UCI machine learning repository.
UCI Machine Learning Repository: Breast Cancer Wisconsin (Diagnostic) Data Set
REFERENCES
Reference Paper-1: Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems
Author’s Name: Seyedali Mirjalil, Amir H. Gandomi, Seyedeh Zahra Mirjalili, Shahrzad Saremi, Hossam Faris and, Seyed Mohammad Mirjalili.
Source: Elsevier, Advances in Engineering Software
Year: 2017
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