کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6862728 | 677015 | 2013 | 19 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Robust feature selection based on regularized brownboost loss
ترجمه فارسی عنوان
انتخاب ویژگی های قوی بر اساس کاهش وزن قهوه ای تنظیم شده
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Feature selection is an important preprocessing step in machine learning and pattern recognition. It is also a data mining task in some real-world applications. Feature quality evaluation is a key issue when designing an algorithm for feature selection. The classification margin has been used widely to evaluate feature quality in recent years. In this study, we introduce a robust loss function, called Brownboost loss, which computes the feature quality and selects the optimal feature subsets to enhance robustness. We compute the classification loss in a feature space with hypothesis-margin and minimize the loss by optimizing the weights of features. An algorithm is developed based on gradient descent using L2-norm regularization techniques. The proposed algorithm is tested using UCI datasets and gene expression datasets, respectively. The experimental results show that the proposed algorithm is effective in improving the classification robustness.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 54, December 2013, Pages 180-198
Journal: Knowledge-Based Systems - Volume 54, December 2013, Pages 180-198
نویسندگان
Pan Wei, Qinghua Hu, Peijun Ma, Xiaohong Su,