کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
406078 678059 2016 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A Robust Extreme Learning Machine for pattern classification with outliers
ترجمه فارسی عنوان
یک دستگاه یادگیری افراطی قوی برای طبقه بندی الگویی با ناپایداری
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

In this paper we introduce a simple and efficient extension of the Extreme Learning Machine (ELM) network (Huang et al., 2006 [19]), which is very robust to label noise, a type of outlier occurring in classification tasks. Such outliers usually result from mistakes during labeling of the data points (e.g. misjudgment of a specialist) or from typing errors during creation of data files (e.g. by striking an incorrect key on a keyboard). The proposed variant of the ELM, henceforth named Robust ELM (RELM), is designed using M-estimators to compute the output weights instead of the standard ordinary least squares (OLS) method. We evaluate the performance of the RELM using batch and recursive learning rules, and also introduce a model selection strategy based on Particle Swarm Optimization (PSO) to find an optimal architecture for datasets contaminated with non-Gaussian noise and outliers. By means of comprehensive computer simulations using synthetic and real-world datasets, we show that the proposed Robust ELM classifiers consistently outperforms the original version.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 176, 2 February 2016, Pages 3–13
نویسندگان
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