طبقه‌بندي کننده چندمنظوره گروه ذرات
محورهای موضوعی : مهندسی برق و کامپیوتر
1 - دانشگاه بیرجند
کلید واژه: بهينه‌سازي چندمنظوره گروه ذراتتشخيص الگوطبقه‌بندي کنندههوش جمعي,
چکیده مقاله :
در اين تحقيق با استفاده از روش بهينه‌سازي چندمنظوره گروه ذرات طبقه‌بندي کننده‌اي ارائه شده است که همزمان قادر به بهينه‌سازي شاخص‌هاي متفاوت و مهمي در تشخيص الگو نظير قابليت اطمينان، نرخ تشخيص صحيح و تعداد ابرصفحه‌هاي لازم براي مرزبندي مؤثر کلاس‌هاي متفاوت در فضاي ويژگي مي‌باشد. در طراحي طبقه‌بندي کننده پيشنهادشده مسايل مهم فوق-برازش و فوق-آموزش نيز برطرف شده است. توانايي دستيابي همزمان به شاخص‌هاي مذکور در ساير طبقه‌بندي کننده‌هاي مبتني بر روش‌هاي هوش جمعي وجود ندارد. نتايج عملي به دست آمده بر روي داده‌هاي آزمايشي نشان مي‌دهند که طبقه‌بندي کننده چندمنظوره پيشنهادي با تخمين جبهة پَرِتو بهترين مجموعه انتخابي از ابرصفحه‌هاي جداکننده کلاس‌هاي مختلف را براي برپايي شرايط دلخواه کاربر در خصوص انتخاب شاخص‌هاي فوق‌الذکر، فراهم مي‌آورد.
A multi-objective particle swarm optimization (MOPSO) algorithm has been used to design a classifier which is able to optimize some important pattern recognition indices concurrently. These are Reliability, Score of recognition, and the number of hyperplanes. The proposed classifier can efficiently approximate the decision hyperplanes for separating the different classes in the feature space and dose not have any over-fitting and over-learning problems. Other swarm intelligence based classifiers do not have the capability of simultaneous optimizing aforesaid indices and they also may suffer the over-fitting problem. The experimental results show that the proposed multi-objective classifier can estimate the optimum sets of hyperplanes by approximating the Pareto-front and provide the favorite user's setup for selecting aforesaid indices.
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