کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6862872 | 1439398 | 2018 | 19 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
High-resolution Self-Organizing Maps for advanced visualization and dimension reduction
ترجمه فارسی عنوان
نقشه های سازماندهی با وضوح بالا برای تجسم پیشرفته و کاهش ابعاد
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کلمات کلیدی
نقشه خودمراقبتی شبکه های عصبی مصنوعی، خوشه بندی و طبقه بندی،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Kohonen's Self Organizing feature Map (SOM) provides an effective way to project high dimensional input features onto a low dimensional display space while preserving the topological relationships among the input features. Recent advances in algorithms that take advantages of modern computing hardware introduced the concept of high resolution SOMs (HRSOMs). This paper investigates the capabilities and applicability of the HRSOM as a visualization tool for cluster analysis and its suitabilities to serve as a pre-processor in ensemble learning models. The evaluation is conducted on a number of established benchmarks and real-world learning problems, namely, the policeman benchmark, two web spam detection problems, a network intrusion detection problem, and a malware detection problem. It is found that the visualization resulted from an HRSOM provides new insights concerning these learning problems. It is furthermore shown empirically that broad benefits from the use of HRSOMs in both clustering and classification problems can be expected.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neural Networks - Volume 105, September 2018, Pages 166-184
Journal: Neural Networks - Volume 105, September 2018, Pages 166-184
نویسندگان
Ayu Saraswati, Van Tuc Nguyen, Markus Hagenbuchner, Ah Chung Tsoi,