با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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Imbalanced credit risk evaluation based on multiple sampling, multiple kernel fuzzy self-organizing map and local accuracy ensemble
ارزیابی ریسک اعتباری نامتوازن بر اساس نمونه گیری چندگانه ، نقشه خود سازماندهی فازی چند هسته ای و گروه دقت محلی-2020 Credit risk evaluation model is generally regarded as a valid method for business risk management. Although the most of literatures about credit risk evaluation always use class-balanced data as sample sets, the study on class-imbalanced datasets is more suitable for actual situation. This paper proposes a new ensemble model to evaluate class-imbalanced credit risk, which integrates multiple sampling, multiple kernel fuzzy self-organizing map and local accuracy ensemble. To preprocess imbalanced sample sets of credit risk evaluation, multiple sampling approaches (synthetic minority over-sampling technique, under sampling and hybrid sampling) are improved and integrated to acquire balanced datasets. To construct more suitable base classifiers, multiple kernel functions (Gaussian, Polynomial and Sigmoid) respectively are used to improve fuzzy self-organizing map. Then, the balanced sample sets are respectively processed by the improved base classifiers to acquire different prediction results. The local accuracy ensemble method is employed to dynamically synthesize these prediction results to obtain final result. The new ensemble model can further avoid over-fitting and information loss, be more suitable to handle the dataset including different financial indicators, and acquire the stable and satisfactory prediction result for imbalanced credit risk evaluation In the empirical research, this paper adopts the financial data from Chinese listed companies, and makes the comparative analysis with the relative models step by step. The results can prove that the new ensemble model presented by this article has better performance than other methods in terms of evaluating the imbalanced credit risk.© 2020 Elsevier B.V. All rights reserved. Keywords: Credit risk evaluation | Class-imbalanced data | Multiple sampling | Multiple kernel fuzzy self-organizing map | Local accuracy ensemble |
مقاله انگلیسی |
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Data analysis of multi-dimensional thermophysical properties of liquid substances based on clustering approach of machine learning
تجزیه و تحلیل داده ها از خصوصیات حرارتی فیزیکی چند بعدی مواد مایع بر اساس روش خوشه بندی یادگیری ماشین-2019 In order to develop an efficient framework for global screening in the material exploration, we performed a
clustering analysis of machine learning on the multi-dimensional thermophysical properties of the liquid substances.
Data mining using a self-organizing map (SOM) based on the unsupervised learning was employed to
project high-dimensional thermophysical data onto a low-dimensional space. Here we adopted 98 liquid substances
with eight thermo-physical properties for the SOM training in order to group the liquid substances. The
present SOM-clustering approach properly categorized liquid substances according to the chemical species
characterized by the functional groups. Keywords: Self-organizing map | Clustering analysis | Machine learning | Thermophysical properties | Heat medium |
مقاله انگلیسی |
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Characterization of variability of air particulate matter size profiles recorded by optical particle counters near a complex emissive source by use of Self-Organizing Map algorithm
خصوصیات تنوع پروفیل اندازه ذرات هوا که توسط پیشخوان های ذرات نوری در نزدیکی یک منبع نشر پیچیده با استفاده از الگوریتم نقشه خود سازماندهی ثبت شده-2019 In the present study we propose the application of a procedure of data analysis based on the Self-Organizing Map
algorithm and k-means clustering in series (1st level and 2nd level abstraction respectively) as a strategy to
identify recurrent ambient air particulate matter (PM) size profiles starting from the elaboration of high frequency
data recorded by an Optical Particle Counter (OPC). We tested the strategy on data deriving from a three months
survey at a residential site in proximity to an integral cycle steel plant in Trieste (NE Italy).
We were able to identify four clusters representing recurrent PM class profiles whose meaning has been
interpreted and confirmed by correlation to “external data”, i.e. data not used to build the model, registered by
other devices (meteorological and pollutant monitoring stations). The four clusters were found to be related to
two different plant type of emissions (sources) and to two different site background profiles, respectively. The
powerful visualization features of SOM map allowed to describe and characterize the variability of size distribution
of PM in a concise form. The clustered SOM being built for one measuring station, proved to be helpful for
the analysis of OPC data collected at another location close to the industrial plant. Moreover, occasional episodes
of Saharan dusts could be identified as outliers with respect to local particulate and discussed in terms of size
distribution. Eventually, by means of an animated graph, we propose a method to visualize the PM experimental
data evolution during the day using the PM cluster profiles as a legend. Keywords: Self-Organizing Map | Pattern recognition | Ambient air | Particulate matter | Optical particle counter |
مقاله انگلیسی |
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یک رویکرد داده کاوی جدید در مورد تأثیر رسانههای اجتماعی برای نظارت بر نظر مصرفکنندهی سیتاگلیپتین
سال انتشار: 2015 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 19 یک روش داده کاوی جدید برای ارزیابی تجربهی مصرف داروی سیتاگلیپتین (نام تجاری Januvia) توسط بیماران مبتلا به دیابت نوع 2 توسعه داده شد. به این منظور، یک چارچوب تجزیه و تحلیل دومرحلهای ابداع کردیم. تجزیه و تحلیل اکتشافی اولیه با استفاده از نقشههای خود سازماندهی انجام شد تا ساختارهای مبتنی برنظرات مصرفکننده در گزارشهای انجمن تعیین شود. نتایج تلفیقی از خوشههای کاربر و نظر همبستهی آنها (مثبت یا منفی) در مورد دارو بود. مدلسازیهای متعاقب با استفاده از روش تجزیه و تحلیل شبکه برای تعیین کاربران تأثیرگذار در بین اعضای انجمن مورد استفاده قرار گرفت. این یافتهها میتواند راههای جدیدی از تحقیقات برای جمعآوری سریع دادهها، بازخورد و تجزیه و تحلیل باز کند که به نوبهی خود موجب بهبود نتایج و راهحلهای بهداشت عمومی و بازخورد مهم برای تولیدکننده میشوند.
واژگان: استخراج داده، تجزیه و تحلیل شبکه، نقشه خود سازماندهی، رسانههای اجتماعی.
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