انتخاب ویژگی نیمهنظارتی مبتنیبر خودرمزنگار گراف با حفظ ساختار محلی-گسترده | ||
| پژوهش های نظری و کاربردی هوش ماشینی | ||
| مقاله 10، دوره 3، شماره 1، شهریور 1404، صفحه 163-177 اصل مقاله (1.05 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22034/abmir.2025.23363.1140 | ||
| نویسندگان | ||
| محمدجواد رضایی1؛ مهدیآقا صرام2؛ راضیه شیخ پور* 3 | ||
| 1دانشجوی دکتری، دانشکده مهندسی کامپیوتر، دانشگاه یزد، یزد، ایران | ||
| 2دانشیار، دانشکده مهندسی کامپیوتر، دانشگاه یزد، یزد، ایران | ||
| 3دانشیار، گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه اردکان، اردکان، ایران | ||
| چکیده | ||
| پردازش دادههای با ابعاد بالا چالش مهمی در حوزههای مختلف است و انتخاب ویژگی بهعنوان روشی مؤثر برای کاهش ابعاد، نقش کلیدی در بهبود عملکرد مدلهای یادگیری ماشین دارد. از آنجا که برچسبگذاری دادهها پرهزینه و زمانبر است، انتخاب ویژگی نیمهنظارتی که از دادههای بدون برچسب نیز استفاده کند، اهمیت ویژهای دارد. در این مقاله، یک روش انتخاب ویژگی نیمهنظارتی تنک مبتنی بر خودرمزنگار گراف ارائه میشود که دو نوآوری اصلی دارد: (1) ترکیب خودرمزنگار برای حفظ ساختار کلی داده و گراف طیفی نیمهنظارتی برای حفظ ساختار محلی و اطلاعات برچسب (2) اعمال منظمسازی نرم-L_(2,1) برروی ماتریس وزن رمزگذار تا سطرهای غیرمؤثر به صفر میل کرده و ویژگیهای نامرتبط بهطور خودکار حذف شوند. بهینهسازی مسئله با الگوریتم گرادیان و پسانتشار انجام شده و مشتق منظمسازی در بهروزرسانی پارامترها لحاظ میشود؛ بدین ترتیب انتخاب ویژگی به صورت درونمدلی و همزمان با آموزش شبکه انجام میگیرد. روش پیشنهادی بر روی شش مجموعهداده استاندارد UCI شامل ORL، ATT، WBCD، WDBC، QSAR و پارکینسون ارزیابی و با پنج روش مرجع مقایسه شد. معیار ارزیابی، دقت طبقهبندی با استفاده از ماشین بردار پشتیبان و k-نزدیکترین همسایه بود. نتایج دو طبقهبند برروی شش مجموعه داده به ترتیب 78/0، 88/0، 98/0، 97/0، 81/0، 91/0 و 75/0، 92/0، 97/0، 94/0، 82/0، 92/0 نشان داد که روش پیشنهادی در اغلب موارد عملکرد برتری دارد. این یافتهها تأیید میکنند که چارچوب پیشنهادی با بهرهگیری همزمان از ساختار داده و منظمسازی تنک، قادر به انتخاب مجموعهای کارآمد از ویژگیها در شرایط نیمهنظارتی است. | ||
| کلیدواژهها | ||
| انتخاب ویژگی نیمهنظارتی؛ خودرمزنگار؛ مدلهای تنک؛ منظمسازی نرمL_(2؛ 1) | ||
| عنوان مقاله [English] | ||
| Semi-supervised Sparse Feature Selection based on Graph Autoencoder by Preservation of Broad and Local Data Structures | ||
| نویسندگان [English] | ||
| MohammadJavd Reezaei1؛ MahdiAgha Sarram2؛ Razieh Sheikhpour3 | ||
| 1Phd candidate, Computer Engineering Department, Yazd University, Yazd, Iran | ||
| 2Associate Professor, Computer Engineering Department, Yazd University, Yazd, Iran | ||
| 3Associate Professor, Department of Computer Engineering, Faculty of Engineering, Ardakan University, Ardakan, Iran | ||
| چکیده [English] | ||
| Processing and analyzing high-dimensional data is a significant challenge in many domains, and feature selection, as an effective dimension reduction method, plays a key role in improving the performance of machine learning models. Given that in the real world, labeling large volumes of data is costly and time-consuming, semi-supervised feature selection methods that can leverage valuable information from unlabeled data alongside labeled data have gained considerable importance. In this paper, a novel sparse semi-supervised feature selection framework is introduced, which simultaneously preserves the broad and local structures of data as well as the information from available labels. The proposed framework by optimizing a comprehensive objective function comprising an autoencoder reconstruction term, an L_(2,1)-norm regularization term for sparsity, and a term based on the semi-supervised spectral graph, selects an optimal subset of features. To solve this optimization problem, a gradient-based backpropagation algorithm is employed, and its convergence has been empirically investigated and confirmed. Extensive evaluations on six standard datasets and comparison of the results with several prominent previous methods demonstrate the significant superiority of the proposed framework in improving classification accuracy and selecting more effective features under semi-supervised conditions. | ||
| کلیدواژهها [English] | ||
| Semi-supervised, Feature selection, Auto encoder, Sparse models, L_(2, 1)-norm | ||
| مراجع | ||
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