ارائه يک نسخه جديد از الگوريتم مورچگان باينری به منظور حل مسأله انتخاب ويژگی
محورهای موضوعی : مهندسی برق و کامپیوتر
شيما کاشف
1
(دانشگاه شهید باهنر کرمان)
حسین نظام‌آبادی‌پور
2
(دانشگاه شهید باهنر کرمان)
کلید واژه: انتخاب ويژگي الگوريتم مورچگان باينري طبقه‌بندي کاهش بعد ويژگي,
چکیده مقاله :
استفاده از الگوریتم‌های ابتکاری یک انتخاب مناسب برای حل مسایل بهینه‌سازی است. در اين مقاله نسخه بهبوديافته‌اي از الگوريتم بهينه‌ساز مورچگان باينري براي حل مسأله انتخاب ويژگي ارائه شده است. نسخه پيشنهادي خصوصيات الگوريتم جمعيت مورچه گسسته و الگوريتم مورچه باينري را به صورت توأمان در خود دارد. کارايي روش پيشنهادي روي 12 پايگاه داده استاندارد در موضوع طبقه‌بندي بررسي و نتايج با چند الگوريتم مطرح در اين زمينه شامل بهينه‌ساز جمعيت مورچگان گسسته و باينري مقايسه شده است. نتايج بيانگر کارايي مناسب الگوريتم پيشنهادي است.
The use of metaheuristic algorithms is a good choice for solving optimization problems. In this paper, a novel feature selection algorithm based on Ant Colony Optimization (ACO), called Advanced Binary ACO (ABACO), is presented. This algorithm is an advanced version of binary ant colony optimization, which attempts to solve the problems of ACO and BACO algorithms by combination of these two. The performance of proposed algorithm is compared to the performance of Binary Genetic Algorithm (BGA), Binary Particle Swarm Optimization (BPSO), and some prominent ACO-based algorithms on the task of feature selection on 12 well-known UCI datasets. Simulation results verify that the algorithm provides a suitable feature subset with good classification accuracy using a smaller feature set than competing feature selection methods.
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