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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

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Chaotic particle swarm optimization with mutation for classification.

Zahra Assarzadeh¹, Ahmad Reza Naghsh-Nilchi²

¹M.Sc. Student, Department of Artificial Intelligence, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran and The lecturer of Payam Higher Education Institution of Golpaygan, Isfahan, Iran.

Journal of Medical Signals and Sensors

|February 25, 2015

Summary

A novel chaotic particle swarm optimization with mutation enhances pattern classification. This improved algorithm overcomes local minima, leading to superior accuracy in medical datasets compared to other methods.

Keywords:

Decision hyperplanes medical database classification particle swarm optimization pattern recognition

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Area of Science:

Computational Intelligence
Machine Learning
Medical Data Analysis

Background:

Particle Swarm Optimization (PSO) algorithms often suffer from premature convergence to local minima.
Effective classification and feature selection are crucial for medical datasets.

Purpose of the Study:

To propose a chaotic particle swarm optimization with mutation-based classifier (CPSO-MC) for enhanced pattern classification.
To introduce a feature selection method using a binary version of CPSO-MC for dimensionality reduction in medical data.

Main Methods:

Developed a chaotic PSO algorithm incorporating mutation operators to prevent local minima and refine solutions.
Implemented a binary PSO for feature selection to remove irrelevant data and reduce dimensionality.
Evaluated the CPSO-MC on Wisconsin diagnostic breast cancer, Wisconsin breast cancer, and heart-statlog datasets.

Main Results:

The CPSO-MC demonstrated superior performance compared to k-nearest neighbor, PSO-classifier, genetic algorithm, and Imperialist Competitive Algorithm-classifier.
Key performance metrics included accuracy, sensitivity, specificity, and Matthews's correlation coefficient, all favoring the proposed method.

Conclusions:

The proposed chaotic particle swarm optimization with mutation-based classifier effectively addresses limitations of standard PSO.
The CPSO-MC offers a robust and high-performing solution for medical data classification and feature selection.