يك روش دومرحلهاي براي تركيب طبقه‌بندها
الموضوعات :
سیدحسن نبوی کریزی
1
(آموزشكده فني حرفه‌اي شهيد منتظري مشهد)
احسان‌اله کبیر
2
(دانشگاه تربیت مدرس)
الکلمات المفتاحية: ايجاد گوناگونياختلاط خبره‌هاگروه ذراتتركيب طبقه‌بندهاتركيب خطيبهينه‌سازي,
ملخص المقالة :
يادگيري دسته‌جمعي، يک رويکرد مؤثر در يادگيري ماشيني است كه در آن با تركيب نتايج چند طبقه‌بند سعي مي‌شود تقريب بهتري از يك طبقه‌بند بهينه فراهم شود. براي آنكه تركيب نتايج طبقه‌بندها مفيد واقع شود بايد طبقه‌بندهاي پايه ضمن برخورداري از كارآيي قابل قبول، داراي خطاهاي متفاوتي بوده و قاعده مناسبي براي تركيب نتايج آنها به كار گرفته شود. در اين مقاله يك روش دومرحلهاي براي تركيب نتايج طبقه‌بندها پيشنهاد مي‌شود كه در مرحله اول آن، با روش اختلاط خبره‌ها يك مجموعه طبقه‌بند با خطاهاي متفاوت ايجاد مي‌شود و در مرحله دوم با استفاده از الگوريتم بهينه‌سازي گروه ذرات، وزن‌هاي بهينه براي تركيب خطي نظرات آنها پيدا مي‌شوند. نتايج آزمايش‌هاي ما بر روي چند مجموعه داده متداول، نشان مي‌دهند كه روش پيشنهادي ما باعث افزايش كارآيي سيستم طبقه‌بندي مركب نسبت به روش‌هاي يادگيري مستقل و روش اختلاط خبره‌ها مي‌شود.
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