دانلود مقاله انگلیسی رایگان:درختان عصبی با دانش همتا به همتا و سرور به مشتری انتقال مدل برای طبقه بندی داده های بعدی - 2019
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  • Neural trees with peer-to-peer and server-to-client knowledge transferring models for high-dimensional data classification Neural trees with peer-to-peer and server-to-client knowledge transferring models for high-dimensional data classification
    Neural trees with peer-to-peer and server-to-client knowledge transferring models for high-dimensional data classification

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Neural trees with peer-to-peer and server-to-client knowledge transferring models for high-dimensional data classification


    ترجمه فارسی عنوان مقاله:

    درختان عصبی با دانش همتا به همتا و سرور به مشتری انتقال مدل برای طبقه بندی داده های بعدی


    منبع:

    Sciencedirect - Elsevier - Expert Systems With Applications, 137 (2019) 281-291: doi:10:1016/j:eswa:2019:07:003


    نویسنده:

    Shadi Abpeykar, Mehdi Ghatee


    چکیده انگلیسی:

    Classification of the high-dimensional data by a new expert system is followed in the current paper. The proposed system defines some non-disjoint clusters of highly relevant features with the least inner- redundancy. For each cluster, a neural tree is implemented exploiting an Extreme Learning Machine (ELM) together an inference engine in any node. The derived classification rules from ELM are stored in the rule- base of the inference engine to recognize the classes. A majority voting is used to unify the results of the different neural trees. This structure is refereed as the Forest of Extreme Learning Machines with Rule- base Transferring (FELM-RT). The contribution of FELM-RT is to decrease the duplicated computations by using two novel interaction models between the neural trees. In the first interaction model, namely Peer- to-Peer (P2P) model, each node can share its rule-base with the other nodes of the various neural trees. In the second that is referred as Server-to-Client (S2C) model, a neural tree that works on a cluster with the best relevancy and redundancy, shares the rules with the other neural trees. In both of the models, a fuzzy aggregation technique is used to adjust the certainty of the rules. The processing time of FELM-RT decreases essentially and it improves the classification accuracy. The high results of F-measure and G- mean, show that FELM-RT classifies the high-dimensional datasets without over-fitting. The comparison between FELM-RT and some state-of-the-art classifiers reveals that FELM-RT overcomes them specially on the datasets with more than 3 million features.
    Keywords: Neural tree | Rule-base transferring | Feature clustering | Extreme learning machine | Communication models


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 11
    حجم فایل: 1375 کیلوبایت

    قیمت: رایگان


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