A multi-objective optimization approach for online streaming feature selection using fuzzy Pareto dominance
سال انتشار: 1402
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 61
فایل این مقاله در 24 صفحه با فرمت PDF قابل دریافت می باشد
- صدور گواهی نمایه سازی
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_KJMMRC-13-1_030
تاریخ نمایه سازی: 28 آبان 1402
چکیده مقاله:
Feature selection is one of the most important tasks in machine learning. Traditional feature selection methods are inadequate for reducing the dimensionality of online data streams because they assume that the feature space is fixed and every time a feature is added, the algorithm must be executed from the beginning, which in addition to not performing real-time processing, causes many unnecessary calculations and resource consumption. In many real-world applications such as weather forecasting, stock markets, clinical research, natural disasters, and vital-sign monitoring, the feature space changes dynamically, and feature streams are added to the data over time. Existing online streaming feature selection (OSFS) methods suffer from problems such as high computational complexity, long processing time, sensitivity to parameters, and failure to account for redundancy between features. In this paper, the process of OSFS is modeled as a multi-objective optimization problem for the first time. When a feature stream arrives, it is evaluated in the multi-objective space using fuzzy Pareto dominance, where three feature selection methods are considered as our objectives. Features are ranked according to their degree of dominance in the multi-objective space over other features. We proposed an effective method to select a minimum subset of features in a short time. Experiments were conducted using two classifiers and eight OSFS algorithms with real-world datasets. The results show that the proposed method selects a minimal subset of features in a reasonable time for all datasets.
کلیدواژه ها:
Online streaming feature selection ، Fuzzy Pareto dominance ، High-dimensional data ، multi-objective optimization
نویسندگان
Amin Hashemi
Department of Computer Engineering, Faculty of Engineering, Yazd University, Yazd, Iran
Mohammad-Reza Pajoohan
Department of Computer Engineering, Faculty of Engineering, Yazd University, Yazd, Iran
Mohammad Bagher Dowlatshahi
Department of Computer Engineering, Faculty of Engineering, Lorestan University, Khorramabad, Iran.
مراجع و منابع این مقاله:
لیست زیر مراجع و منابع استفاده شده در این مقاله را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود مقاله لینک شده اند :