یک الگوریتم انتخاب ویژگی برخط در جریان داده‌ها با استفاده از اطلاعات متقابل چندمتغیره
محورهای موضوعی : مهندسی برق و کامپیوترمریم رحمانی نیا 1 , پرهام مرادی 2
1 - دانشگاه آزاد اسلامی واحد قصرشیرین
2 - دانشگاه کردستان
کلید واژه: انتخاب ویژگیداده‌های آموزشی برخطاطلاعات متقابلمتغیر تصادفی مشترک,
چکیده مقاله :
امروزه در بسیاری از مسایل دنیای واقعی همچون شبکه‌های اجتماعی، با جریان داده مواجه هستیم که در هر لحظه داده جدیدی به مجموعه داده‌های موجود اضافه می‌شود. از آنجا که کارایی بیشتر الگوریتم‌های داده‌کاوی با افزایش ابعاد داده‌ها کاهش می‌یابد، تحلیل این جریان داده‌ها در سال‌های اخیر به یکی از مسایل مهم در داده‌کاوی تبدیل شده است. روش‌های انتخاب ویژگی در جریان داده‌های برخط، روش‌های کارآمدی هستند که با حذف ویژگی‌های افزونه و نامربوط باعث کاهش ابعاد کلان داده‌ها و در نتیجه بهبود کارایی الگوریتم‌ها می‌شوند. از چالش‌های اساسی در رابطه با الگوریتم‌های انتخاب ویژگی برخط، در دسترس نبودن همه داده‌ها قبل از شروع الگوریتم، مقیاس‌پذیری، دقت ویژگی‌های انتخاب‌شده و اندازه زیرمجموعه انتخابی را می‌توان نام برد. تا کنون الگوریتم‌های انتخاب ویژگی موجود تنها توانسته‌اند بخش محدودی از این چالش‌ها را به صورت هم‌زمان مرتفع کنند. به همین منظور در این مقاله یک راهکار انتخاب ویژگی برخط به نام MMIOSFS با استفاده از اطلاعات متقابل ارائه داده‌ایم که حد واسط بهتری را میان چالش‌های ذکرشده به دست می‌آورد. در روش پیشنهادی در ابتدا مجموعه ویژگی‌ها با استفاده از تکنیک متغیرهای تصادفی توأم به یک ویژگی نگاشت و سپس اطلاعات متقابل ویژگی جدید با برچسب به عنوان میزان ارتباط مجموعه ویژگی‌های اولیه در نظر گرفته می‌شود. کارایی روش پیشنهادی با چند الگوریتم انتخاب ویژگی برخط با استفاده از دسته‌بندهای مختلف مورد ارزیابی قرار گرفته و نتایج به دست آمده نشان می‌دهد الگوریتم پیشنهادی معمولاً حد واسط بهتری میان چالش‌ها به دست می‌آورد.
Today, in many real-world applications, such as social networks, we are faced with data streams which new data is appeared every moment. Since the efficiency of most data mining algorithms decreases with increasing data dimensions, analysis of the data has become one of the most important issues recently. Online stream feature selection is an effective approach which aims at removing those of redundant features and keeping relevant ones, leads to reduce the size of the data and improve the accuracy of the online data mining methods. There are several critical issues for online stream feature selection methods including: unavailability of the entire feature set before starting the algorithm, scalability, stability, classification accuracy, and size of selected feature set. So far, existing methods have only been able to address a few numbers of these issues simultaneously. To this end, in this paper, we present an online feature selection method called MMIOSFS that provides a better tradeoff between these challenges using Mutual Information. In the proposed method, first the feature set is mapped to a new feature using joint Random variables technique, then the mutual information of new feature with the class label is computed as the degree of relationship between the features set. The efficiency of the proposed method was compared to several online feature selection algorithms based on different categories. The results show that the proposed method usually achieves better tradeoff between the mentioned challenges.
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