{"id":139,"date":"2019-12-01T06:45:55","date_gmt":"2019-12-01T06:45:55","guid":{"rendered":"http:\/\/norc.aut.ac.ir\/?p=139"},"modified":"2019-12-01T06:45:55","modified_gmt":"2019-12-01T06:45:55","slug":"neural-trees-with-peer-to-peer-and-server-to-client-knowledge-transferring-models-for-high-dimensional-data-classification","status":"publish","type":"post","link":"https:\/\/norc.aut.ac.ir\/index.php\/2019\/12\/01\/neural-trees-with-peer-to-peer-and-server-to-client-knowledge-transferring-models-for-high-dimensional-data-classification\/","title":{"rendered":"Neural trees with peer-to-peer and server-to-client knowledge transferring models for high-dimensional data classification"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">By: Shadi Abpeykar, &amp; Mehdi Ghatee<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Published: Expert Systems with Applications, Volume 137, 15 December 2019, Pages 281-291<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Abstract:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">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.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Highlights<\/h2>\n\n\n\n<ul class=\"wp-block-list\"><li>Clustering the features into some sets with highly related features and low innerredundancy.<\/li><li>Defining a neural tree exploiting an ELM and an inference engine in any node.<\/li><li>Transferring the rules extracted from any ELM to other nodes of neural tree.<\/li><li>Proposing Peer-to-Peer (P2P) and Server-Client (SC) models for knowledge transferring.<\/li><li>Classifying high dimensional data with high accuracy and without overfitting.<\/li><\/ul>\n","protected":false},"excerpt":{"rendered":"<p>By: Shadi Abpeykar, &amp; Mehdi Ghatee Published: Expert Systems with Applications, Volume 137, 15 December 2019, Pages 281-291 Abstract: 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&#8230;<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_kad_post_transparent":"","_kad_post_title":"","_kad_post_layout":"","_kad_post_sidebar_id":"","_kad_post_content_style":"","_kad_post_vertical_padding":"","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"_kad_post_classname":"","footnotes":""},"categories":[7],"tags":[],"class_list":["post-139","post","type-post","status-publish","format-standard","hentry","category-published-paper"],"_links":{"self":[{"href":"https:\/\/norc.aut.ac.ir\/index.php\/wp-json\/wp\/v2\/posts\/139","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/norc.aut.ac.ir\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/norc.aut.ac.ir\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/norc.aut.ac.ir\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/norc.aut.ac.ir\/index.php\/wp-json\/wp\/v2\/comments?post=139"}],"version-history":[{"count":0,"href":"https:\/\/norc.aut.ac.ir\/index.php\/wp-json\/wp\/v2\/posts\/139\/revisions"}],"wp:attachment":[{"href":"https:\/\/norc.aut.ac.ir\/index.php\/wp-json\/wp\/v2\/media?parent=139"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/norc.aut.ac.ir\/index.php\/wp-json\/wp\/v2\/categories?post=139"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/norc.aut.ac.ir\/index.php\/wp-json\/wp\/v2\/tags?post=139"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}