يك روش يادگيري جديد براي افزايش كارايي سيستم طبقه‌بندي مركب
محورهای موضوعی : مهندسی برق و کامپیوتر
سیدحسن نبوی کریزی
1
,
احسان‌اله کبیر
2
1 - دانشگاه تربیت مدرس
2 - دانشگاه تربیت مدرس
کلید واژه: طبقه‌بندهايادگيري دسته‌جمعيگوناگوني در خطاهمبستگي منفيشبكه عصبي,
چکیده مقاله :
يادگيري دسته‌جمعي يک رويکرد مؤثر در يادگيري ماشيني است كه به منظور بهبود كارايي سيستم بازشناسي الگو استفاده مي‌شود. براي آنكه اين نوع يادگيري مفيد واقع شود بايد خطاهاي طبقه‌بندهاي پايه با يكديگر متفاوت باشند. راهكارهاي ايجاد تفاوت در خطا، به دو گروه ضمني و صريح تقسيم مي‌شوند. در اين تحقيق روش جديدي از نوع صريح براي ايجاد گوناگوني در طبقه‌بندهاي يك سيستم مركب ارائه مي‌شود. در اين روش، معيار تازه‌اي از گوناگوني در فرايند يادگيري سيستم مركب به كار گرفته مي‌شود. در روش پيشنهادي، شباهت بين خطاي هر طبقه‌بند با طبقه‌بندهاي ديگر به صورت يك مؤلفه در تابع خطاي آن طبقه‌بند منظور شده و در الگوريتم يادگيري آن ايفاي نقش مي‌كند. نتايج آزمايش‌هاي ما بر روي چند مجموعه داده متداول، براي حالتي كه طبقه‌بندهاي پايه از نوع شبكه‌هاي عصبي باشند، نشان مي‌دهند كه روش پيشنهادي ما باعث افزايش كارايي سيستم طبقه‌بندي مركب نسبت به روش‌هاي مشابه آن مي‌شود.
The combination of multiple classifiers is shown to be suitable for improving the performance of pattern recognition systems. Combining multiple classifiers is only effective if the individual classifiers are accurate and diverse. The methods have been proposed for diversity creation can be classified into implicit and explicit methods. In this paper, we propose a new explicit method for diversity creation. Our method adds a new penalty term in learning algorithm of neural network ensembles. This term for each network is the product of its error and the sum of other networks errors. Experimental results on different data sets show that proposed method outperforms the independent training and the negative correlation learning methods.
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