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Register a new userDevil's Advocate: Novel Boosting Ensemble Method from Psychological Findings for Text Classification
داعية الشيطان: طريقة تعزز NEWSTING من النتائج النفسية لتصنيف النص
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نقدم شكل جديد من طريقة الفرقة - داعية الشيطان، والذي يستخدم نموذجا مختلفا عمدا لفرض السفلات الأخرى داخل الفرقة للتعاون بشكل أفضل.تتكون طريقتنا من إعدادات تدريبية مختلفة: يتبع المرء عملية التدريب التقليدية (القاعدة)، والآخر يتم تدريبه بواسطة ملصقات تم إنشاؤها بشكل مصطنع (DevAfv).بعد تدريب النماذج، يتم ضبط نماذج القواعد بشكل جيد من خلال وظيفة خسارة إضافية، والتي تستخدم نموذج DevAdh كعائق.في اتخاذ قرار نهائي، يبلغ نموذج الفرقة المقترح درجات نماذج المعايير ثم يطرح نتيجة نموذج DevAdh.يحسن نموذج DevAPAdh الأداء العام للنماذج الأخرى داخل الفرقة.بالإضافة إلى إطار عملنا الذي يعتمد على الخلفية النفسية، فإنه يظهر أيضا أداء مماثل أو محسن على 5 مهام تصنيف النص عند مقارنته بطرق الفرقة التقليدية.
We present a new form of ensemble method--Devil's Advocate, which uses a deliberately dissenting model to force other submodels within the ensemble to better collaborate. Our method consists of two different training settings: one follows the conventional training process (Norm), and the other is trained by artificially generated labels (DevAdv). After training the models, Norm models are fine-tuned through an additional loss function, which uses the DevAdv model as a constraint. In making a final decision, the proposed ensemble model sums the scores of Norm models and then subtracts the score of the DevAdv model. The DevAdv model improves the overall performance of the other models within the ensemble. In addition to our ensemble framework being based on psychological background, it also shows comparable or improved performance on 5 text classification tasks when compared to conventional ensemble methods.
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