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All Journal JOIV : International Journal on Informatics Visualization Indonesian Journal of Electrical Engineering and Computer Science
Abdul Aziz, Nor Hidayati
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Computer Science & IT

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Hybrid feature selection of microarray prostate cancer diagnostic system Ali, Nursabillilah Mohd; Hanafi, Ainain Nur; Karis, Mohd Safirin; Shamsudin, Nur Hazahsha; Shair, Ezreen Farina; Abdul Aziz, Nor Hidayati
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1884-1894

Abstract

DNA microarray prostate cancer diagnosis systems are widely used, and hybrid feature selection methods are applied to select optimal features to address the high dimensionality of the dataset. This work proposes a new hybrid feature selection method, namely the relief-F (RF)-genetic algorithm (GA) with support vector machine (SVM) classification method. The aim is to evaluate the performance of the proposed method in terms of accuracy, computation time, and the number of selected features. The method is implemented using Python in PyCharm and is evaluated on a DNA microarray prostate cancer. The outcome of this work is a performance comparison table for the proposed methods on the dataset. The performance of GA, particle swarm optimization (PSO), and whale optimization algorithm (WOA) is compared in terms of accuracy, computation time, and the number of selected features. Results show that GA has the highest average accuracy (91.17%) compared to PSO (90.52%) and WOA (85.74%). GA outperforms PSO and WOA due to its superior convergence properties and better alignment with complex problems.
Understanding Search Behavior in the Simulated Kalman Filter Algorithm Abdul Aziz, Nor Hidayati; Jing Hao, Ooi
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.3538

Abstract

In computational optimization, metaheuristic algorithms are crucial for solving complex and dynamic problems. It is important to fully understand how an algorithm searches, as it helps to improve the algorithm and its applications in various domains. This paper provides a detailed analysis of how the Simulated Kalman Filter algorithm searches for optimal solutions. The SKF algorithm is an optimization method inspired by the Kalman filter estimation techniques. The algorithm was introduced in 2015 to address unimodal problems. Since its inception, the SKF algorithm has undergone improvements and is used to solve a range of optimization problems. Our study aims to bridge the gap in existing research by investigating how SKF effectively balances the search space exploration and known solution exploitation. Through systematic experimentation using the Brown function as a benchmark, we explored the social dynamics and movement style of the SKF algorithm, in addition to the convergence efficiency and accuracy. When we applied the same approach as suggested in the referenced paper, we gained insights into SKF’s unique strengths and limitations of SKF when compared to other algorithms. The findings illustrate SKF’s unique capabilities in handling the exploration-exploitation trade-off. This study helps to set the foundation for creating more advanced algorithms and optimization strategies in the future. Future research will examine how enhancements to the SKF algorithm impact and enhance its search behavior.
Co-Authors Ali, Nursabillilah Mohd Hanafi, Ainain Nur Jing Hao, Ooi Karis, Mohd Safirin Shair, Ezreen Farina Shamsudin, Nur Hazahsha