A swarm intelligence based multi-label feature selection method hybridized with a local search strategy
- سال انتشار: 1401
- محل انتشار: فصلنامه مهندسی برق دانشگاه تبریز، دوره: 51، شماره: 4
- کد COI اختصاصی: JR_TJEE-51-4_007
- زبان مقاله: فارسی
- تعداد مشاهده: 217
نویسندگان
Department of Computer Engineering, Islamic Azad University, Sanandaj Branch, Sanandaj, Iran
Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran
Department of Computer Engineering, Islamic Azad University, Sanandaj Branch, Sanandaj, Iran.
چکیده
Multi-label classification aims at assigning more than one label to each instance. Many real-world multi-label classification tasks are high dimensional, leading to reduced performance of traditional classifiers. Feature selection is a common approach to tackle this issue by choosing prominent features. Multi-label feature selection is an NP-hard approach, and so far, some swarm intelligence-based strategies and have been proposed to find a near optimal solution within a reasonable time. In this paper, a hybrid intelligence algorithm based on the binary algorithm of particle swarm optimization and a novel local search strategy has been proposed to select a set of prominent features. To this aim, features are divided into two categories based on the extension rate and the relationship between the output and the local search strategy to increase the convergence speed. The first group features have more similarity to class and less similarity to other features, and the second is redundant and less relevant features. Accordingly, a local operator is added to the particle swarm optimization algorithm to reduce redundant features and keep relevant ones among each solution. The aim of this operator leads to enhance the convergence speed of the proposed algorithm compared to other algorithms presented in this field. Evaluation of the proposed solution and the proposed statistical test shows that the proposed approach improves different classification criteria of multi-label classification and outperforms other methods in most cases. Also in cases where achieving higher accuracy is more important than time, it is more appropriate to use this method.کلیدواژه ها
feature selection, Multi-label classification, Local search strategy, Swarm Intelligence, particle swarm optimizationاطلاعات بیشتر در مورد COI
COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.
کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.