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Neural trees with peer-to-peer and server-to-client knowledge transferring models for high-dimensional data classification.
- Source :
-
Expert Systems with Applications . Dec2019, Vol. 137, p281-291. 11p. - Publication Year :
- 2019
-
Abstract
- • Clustering the features into some sets with highly related features and low innerredundancy. • Defining a neural tree exploiting an ELM and an inference engine in any node. • Transferring the rules extracted from any ELM to other nodes of neural tree. • Proposing Peer-to-Peer (P2P) and Server-Client (SC) models for knowledge transferring. • Classifying high dimensional data with high accuracy and without overfitting. 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09574174
- Volume :
- 137
- Database :
- Academic Search Index
- Journal :
- Expert Systems with Applications
- Publication Type :
- Academic Journal
- Accession number :
- 138272455
- Full Text :
- https://doi.org/10.1016/j.eswa.2019.07.003