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الرئيسية > Hrneuro Fuzzy: Adapting Neuro...>الوصف

Hrneuro Fuzzy: Adapting Neuro Fuzzy Classifier For Recurring Concept Drift Of Evolving Data Streams Using Rough Set Theory And Holoentropy

المصدر: مجلة جامعة الملك سعود - علوم الحاسب والمعلومات
الناشر: جامعة الملك سعود
المؤلف الرئيسي: Nalavade, Jagannath E. (Author)
مؤلفين آخرين: Murugan, T. Senthil (Co-Author)
المجلد/العدد: مج30, ع4
محكمة: نعم
الدولة: السعودية
التاريخ الميلادي: 2018
الصفحات: 498 - 509
DOI: 10.33948/0584-030-004-006
ISSN: 1319-1578
رقم MD: 974491
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: science
مواضيع:
قواعد البيانات | الشبكات العصبية | تدفق المعلومات
كلمات المؤلف المفتاحية:
Data Stream | Neuro Fuzzy | Change Of Detection | Rough Set Theory | Holoentropy Function
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المستخلص: Data stream classification plays a vital role in data mining techniques which extracts the most important patterns from the real world database. Nowadays, many applications like sensor network, video surveillance and network traffic generate a huge amount of data streams. Due to the ambiguity in input data, imprecise input information and concept drift, some problems arise in classifying the data stream. To resolve these problems, we propose a HRNeuro fuzzy system in this paper based on rough set theory and holoentropy function. At first, the input database is given to the PCA algorithm to reduce the dimension of the data. An adaptive neuro fuzzy classifier is utilized where the designing of membership function and rule base are the two important aspects. Then, neuro-fuzzy system undergoes updating when the change of detection occurs between the data streams. Here, the updating behaviour of membership function and rules are performed using rough set theory and holoentropy function. The experimental results are evaluated for the datasets and the performance is analysed by some metrics and compared with the existing systems such as JIT adaptive K-NN and HRFuzzy system. From the result, it is concluded that our proposed fuzzy classifier attains the higher accuracy of 96%, which proves the efficient performance of data stream classification algorithm.
ISSN: 1319-1578

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