یک روش ترکیبی برای یافتن زیرمجموعه ویژگی مؤثر در داده‌های چند برچسبی

نویسندگان

دانشکده فنی مهندسی - دانشگاه شهید باهنر کرمان

چکیده

داده‌های چند برچسبی به داده‌هایی گفته می‌شود که در آن بر خلاف داده‌های تک برچسبی، هر نمونه می‌تواند متعلق به چند کلاس باشد. در سال‌های اخیر، به دلیل رشد روز افزون کاربردهای این داده‌ها، طبقه‌بندی داده‌های چند برچسبی توجه بسیاری از محققان را به خود جلب کرده است. مشابه طبقه‌بندی داده‌های تک برچسبی، در داده‌های چند برچسبی نیز حذف ویژگی‌های زائد و تکراری می‌تواند تأثیر زیادی در بهبود عملکرد طبقه‌بند داشته باشد. در این مقاله، یک روش ترکیبی برای انتخاب ویژگی در داده‌های چند برچسبی ارائه شده است. روش پیشنهادی بر پایه ترکیب یک روش فیلتری و یک روش پیچشی است که در روش پیچشی از الگوریتم‌های فرا ابتکاری استفاده شده است. از آنجا که معمولاً تعداد ویژگی‌های داده‌های چند برچسبی زیاد است، استفاده مستقیم از روش‌های جستجو، برای کشف زیرمجموعه ویژگی بهینه، هزینه محاسباتی بالایی دارد و ممکن است با شکست روبه رو شود. از این رو، ابتدا با استفاده از یک روش فیلتری، ویژگی‌های نامرتبط با کلاس‌ها حذف می‌شوند. سپس، از الگوریتم‌های تکاملی برای انتخاب برجسته‌ترین ویژگی‌ها استفاده می‌شود. در بخش آزمایش‌ها، تعداد قابل توجهی از الگوریتم‌های فرا ابتکاری مشهور بکار گرفته شده و جایگزین روش پیچشی در سامانه پیشنهادی شده است. نتایج به دست آمده نشان می‌دهند که روش پیشنهادی در برابر سایر روش‌های مورد مقایسه، دقت بالاتری دارند و در مواردی که دست یابی به دقت بالاتر، اهمیت بیشتری نسبت به زمان داشته باشد، استفاده از این روش مناسب‌تر است.

کلیدواژه‌ها

عنوان مقاله [English]

A Hybrid Method to Find Effective Subset of Features in Multi-label Datasets

نویسندگان [English]

  • S. Kashef
  • H. Nezamabadi-pour
Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
چکیده [English]

In multi-label data, each instance is associated with a set of labels, instead of one label. Due to the increasing number of modern applications associated with multi-label data, multi-label classification has gained significant attention during recent years. As in single-label data, eliminating redundant and/or irrelevant features plays an important role in improving classification performance. In this paper, a hybrid method for multi-label feature selection problem based on combing filter and wrapper methods is proposed, where meta-heuristic algorithms are employed as the wrapper method. Since, the number of features in multi-label data is usually high, solely employing search algorithms for finding the optimal feature subsets has high computational burden, and is very possible to fail. Hence, irrelevant features are first detected and removed by a filter method. Then, salient features are found among the remained features by the help of meta-heuristic algorithms. A significant number of well-known meta-heuristic algorithms are employed as the wrapper method, in the proposed system. Experiments show that the proposed method obtains better classification results, compared to other algorithms.

کلیدواژه‌ها [English]

  • Multi-label dataset
  • feature selection
  • hybrid methods
  • filter methods
  • wrapper methods
  • meta-heuristic algorithms

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