دانلود رایگان مقاله انگلیسی تنظیم اطلاعات فردی با استفاده از روش های خوشه بندی برای آموزش سیستم خبره - الزویر 2019

عنوان فارسی
تنظیم اطلاعات فردی با استفاده از روش های خوشه بندی برای آموزش سیستم های خبره
عنوان انگلیسی
Subjective data arrangement using clustering techniques for training expert systems
صفحات مقاله فارسی
0
صفحات مقاله انگلیسی
15
سال انتشار
2019
نشریه
الزویر - Elsevier
فرمت مقاله انگلیسی
PDF
نوع مقاله
ISI
نوع نگارش
مقالات پژوهشی (تحقیقاتی)
رفرنس
دارد
پایگاه
اسکوپوس
کد محصول
E9420
رشته های مرتبط با این مقاله
مدیریت، مهندسی کامپیوتر، آمار
گرایش های مرتبط با این مقاله
مدیریت فناوری اطلاعات، الگوریتم ها و محاسبات
مجله
سیستم های کارشناس با نرم افزار - Expert Systems With Applications
دانشگاه
Face Recognition and Artificial Vision Group - Data Science Laboratory - Rey Juan Carlos University - Spain
کلمات کلیدی
داده های متوالی ذهنی، تنظیم داده های ذهنی، ترکیبی از شباهت ها، ارزیابی ریسک رانندگی، پیش بینی ریسک رانندگی
doi یا شناسه دیجیتال
https://doi.org/10.1016/j.eswa.2018.07.058
چکیده

abstract


The evaluation of subjective data is a very demanding task. The classification of the information gathered from human evaluators and the possible high noise levels introduced are ones of the most difficult issues to deal with. This situation leads to adopt individuals who can be considered as experts in the specific application domain. Thus, the development of Expert Systems (ES) that consider the opinion of these individuals have been appeared to mitigate the problem. In this work an original methodology for the selection of subjective sequential data for the training of ES is presented. The system is based on the arrangement of knowledge acquired from a group of human experts. An original similarity measure between the subjective evaluations is proposed. Homogeneous groups of experts are produced using this similarity through a clustering algorithm. The methodology was applied to a practical case of the Intelligent Transportation Systems (ITS) domain for the training of ES for driving risk prediction. The results confirm the relevance of selecting homogeneous information (grouping similar opinions) when generating a ground truth (a reliable signal) for the training of ES. Further, the results show the need of considering subjective sequential data when working with phenomena where a set of rules could not be easily learned from human experts, such as risk assessment.

نتیجه گیری

6. Conclusions


This paper has introduced a novel methodology for the selection of subjective sequential data for the training of ES. The methodology is based on the arrangement of homogeneous information acquired from a group of human experts. It has been proposed two different similarity measures between linearized sequential data, and a novel similarity measure that uses their combination using cluster information.


Several experiments have been achieved in order to illustrate the performance of the methodology. Three of the most representative ones have been included. The first example uses synthetic data to present the methodology. The methodology has been applied to a practical case of the ITS domain where an ES for driving risk prediction has been trained and evaluated through risk evaluations acquired from a group of traffic safety experts. An experiment has been focused on an urban scenario, and another experiment makes use of data collected from an interurban scenario.


The obtained results from these experiments have shown the relevance of selecting homogeneous information for the generation of a reliable ground truth. Also, it could be concluded that the ES trained with homogeneous evaluations performed better when predicting the driving risk. Moreover, these results show the relevance of the use of subjective sequential data when dealing with phenomena where a set of rules could not be easily acquired from human experts, such as risk assessment. In this case, the rules have been properly learned from a set of homogeneous evaluations arranged with the presented methodology obtaining outstanding results.


بدون دیدگاه