Ant Colony Optimization And Crazy Particle Swarm Optimization For Support Vector Classification On High-Dimensional Dataset (Case Study: Prostate Cancer And Colon Cancer)

Firdausanti, Neni Alya (2019) Ant Colony Optimization And Crazy Particle Swarm Optimization For Support Vector Classification On High-Dimensional Dataset (Case Study: Prostate Cancer And Colon Cancer). Masters thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

The data generated by DNA microarray technology can be used to predict and classify genes taken from certain tissues in humans to be classified as cancer or not. Microarray data consists of thousands of variables, but limited observations are available. Support Vector Machine (SVM) is a supervised learning method that can be used for classification on the high-dimensional dataset. There are two problems in SVM classifier that influence the classification accuracy, which are tuning SVM hyper parameters and selecting the best features subset to the SVM classifier. Several approaches have been carried out for the feature selection process and tuning SVM parameter, including a wrapper-based approach. The wrapper-based algorithm used in this research is Crazy Particle Swarm Optimization (CRAZYPSO) and Ant Colony Optimization (ACO). Both algorithms are a computational intelligence-based algorithm that can be used to solve both feature selection and parameter optimization. These algorithms are inspired by animal behavior in the real world. CRAZYPSO calculations are very simple compared to other optimization algorithms. While ACO has several advantages, such as strong robustness, well distributed computing mechanism and easily combined with other methods. This study compares the CRAZYPSO and ACO algorithm in the case of microarray data classification. The microarray datasets used in this study are the prostate dataset and colon dataset. This study uses k-fold cross-validation accuracy to compare the CRAZYPSO and ACO algorithm in the case of microarray data classification using SVM. The result shows that the ABACO algorithm gives a better result in feature selection than the CRAZYPSO algorithm with higher accuracy rate and less selected features, but CRAZYPSO is faster than ABACO to find the best feature subset. This study also shows that the SVM hyper parameter optimized using ACOR algorithm gives higher classification accuracy rate than parameter optimized using CRAZYPSO algorithm does.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Ant Colony Optimization, Microarray, Support Vector Machine Classification, Particle Swarm Optimization
Subjects:	Q Science > Q Science (General) > Q337.3 Swarm intelligence Q Science > QA Mathematics > QA76.9.D343 Data mining. Querying (Computer science)
Divisions:	Faculty of Mathematics, Computation, and Data Science > Statistics > 49101-(S2) Master Thesis
Depositing User:	Neni Alya Firdausanti
Date Deposited:	23 Jul 2026 04:18
Last Modified:	23 Jul 2026 04:18
URI:	http://repository.its.ac.id/id/eprint/67618

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