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رویکرد چندهدفه مبتنی بر روش‌های فرا ابتکاری برای مسئله انتخاب زیرمجموعه ویژگی‌ها | ||
| مدیریت صنعتی | ||
| دوره 13، شماره 2، 1400، صفحه 278-299 اصل مقاله (1.23 M) | ||
| نوع مقاله: مقاله علمی پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/imj.2021.315625.1007809 | ||
| نویسندگان | ||
| امیر دانشور* 1؛ مهدی همایون فر2؛ بیژن نهاوندی3؛ فریبا صلاحی4 | ||
| 1گروه مدیریت فناوری اطلاعات، دانشکده مدیریت، دانشگاه آزاد اسلامی، واحد الکترونیکی، تهران، ایران | ||
| 2گروه مدیریت صنعتی، دانشکده مدیریت وحسابداری، واحد رشت، دانشگاه آزاد اسلامی، رشت، ایران | ||
| 3گروه مدیریت صنعتی، دانشکده مدیریت و اقتصاد، ,واحد علوم و تحقیقات، تهران، ایران | ||
| 4گروه مدیریت صنعتی، دانشکده مدیریت، واحد الکترونیکی، دانشگاه آزاد اسلامی، تهران، ایران | ||
| چکیده | ||
| هدف: پیدا کردن زیرمجموعه‌ای از مجموعه ویژگی‌ها، مسئله‌ای است که در زمینه‌های مختلفی مانند یادگیری ماشین و شناسایی آماری الگوها، کاربرد گسترده‌ای دارد. با توجه به اینکه افزایش تعداد ویژگی‌ها، هزینه محاسباتی سیستم را به‌طور تصاعدی افزایش می‌دهد، این پژوهش به‌دنبال طراحی و پیاده‌سازی سیستم‌هایی با کمترین تعداد ویژگی و کارایی قابل قبول است. روش: با توجه به لزوم جست‌وجوی کارآمد در فضای جواب، در این پژوهش برای انتخاب ویژگی در داده‌های چندکلاسه، از الگوریتم ژنتیک (GA) و الگوریتم ژنتیک با مرتب‌سازی نامغلوب (NSGA II) چندهدفه با هدف افزایش دقت طبقه‌بندی و کاهش تعداد ویژگی‌ها استفاده شده است. روش ارائه شده، بر مبنای دو روش طبقه‌بندی ماشین بردار پشتیبان (SVM) و K نزدیک‌ترین همسایه (KNN) روی 6 مجموعه داده اعتباری به اجرا درآمد و نتایج آن تجزیه و تحلیل شد. یافته‌ها: الگوریتم ژنتیک و الگوریتم ژنتیک با مرتب‌سازی نامغلوب چندهدفه برای افزایش دقت طبقه‌بندی و کاهش تعداد ویژگی‌ها در مسئله انتخاب ویژگی در داده‌های چندکلاسه کارکرد مناسبی دارند. نتایج به‌دست‌آمده، نشان‌دهنده بهبود در دقت طبقه‌بندی، هم‌زمان با کاهش چشمگیر در تعداد ویژگی‌ها در هر دو روش ماشین بردار پشتیبان و نزدیک‌ترین همسایه است. نتیجه‌گیری: با توجه به نتایج، رویکرد پیشنهادشده در این پژوهش برای مسئله انتخاب ویژگی‌ها کارایی بسیار خوبی دارد. | ||
| کلیدواژه‌ها | ||
| برنامه‌ریزی چندهدفه؛ انتخاب زیرمجموعه ویژگی‌ها؛ الگوریتم‌های فرا ابتکاری؛ الگوریتم ژنتیک؛ الگوریتم NSGA II | ||
| عنوان مقاله [English] | ||
| A Multi-objective Approach to the Problem of Subset Feature Selection Using Meta-heuristic Methods | ||
| نویسندگان [English] | ||
| Amir Daneshvar1؛ Mahdi Homayounfar2؛ Bijan Nahavandi3؛ fariba salahi4 | ||
| 1Department of information technology management , Management faculty, Islamic azad university, Electronic Branch, Tehran,Iran | ||
| 2Department of Industrial Management, Faculty of Management and Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran | ||
| 3Department of industrial management, management and economy faculty,Science and Research Branch,,islamic azad university,tehran,iran | ||
| 4Department of Industrial Management, Faculty of Management, Electronic Branch, Islamic Azad University, Tehran, Iran | ||
| چکیده [English] | ||
| Objective: Finding a subset of features is an issue that has been widely used in a variety of fields such as machine learning and statistical pattern recognition. Since increasing the number of features increases the computational cost of a system, it seems necessary to develop and implement systems with minimum features and acceptable efficiency. Methods: Considering objective, it's developmental research and in terms of two Meta-heuristic algorithms, namely genetic algorithm (GA) and multi-objective non-dominated sorting genetic algorithm (NSGA II). The multi-objective method compared to the single-objective method has reduced the number of features to 50% in all instances; it doesn't make much difference in classification accuracy. The proposed method is applied on six datasets of credit data, and the results were analyzed using two common classifiers namely, support vector machine (SVM) and K-nearest neighbors (KNN). Comparing two classifiers applied on datasets, K- nearest neighbors (KNN) compared to the support vector machine (SVM) has shown relatively better performance in increasing the classification accuracy and reducing the number of attributes. Results: Genetic algorithm and multi objective non-dominated sorting genetic algorithm have a good performance in increasing the accuracy of classification and reducing the number of attributes in feature selection problem of multi-class data. The results also indicate an increase in classification accuracy, simultaneously with a significant decrease in the number of features in both KNN and SVM methods. Conclusion: According to the results, the proposed approach has a high efficiency in features selection problem. | ||
| کلیدواژه‌ها [English] | ||
| Multi-objective programming, Feature subset selection, Meta-heuristic algorithm, Genetic algorithm, NSGA II algorithm | ||
| مراجع | ||
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رزمی، جعفر؛ حیدریه، سید عبدالله؛ شهابی، علی (1393). توسعه مدل پذیرش فناوری در بانکداری ایران (پژوهشی پیرامون بانک رفاه). مدیریت صنعتی، 6 (3)، 471-490.
نصرتی ناهوک، حسن؛ افتخاری، مهدی (1392). یک روش جدید برای انتخاب ویژگی مبتنی بر منطق فازی. هوش محاسباتی در مهندسی برق، 4 (1)، 71-84.
همایونفر، مهدی؛ باقرسلیمی، سعید؛ نهاوندی، بیژن؛ ایزدی شیجانی، کاوه (1397). شبیه‌سازی مبتنی بر عامل شبکه تأمین شرکت ملی پخش فراورده‌های نفتی در قالب سیستم انطباقی پیچیده بهمنظور دستیابی به سطح موجودی بهینه. مدیریت صنعتی، 10 (4)، 607-630.
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