Subgroup Preference Neural Network.

Elgharabawy, A; Prasad, M; Lin, C-T

Subgroup Preference Neural Network.

Elgharabawy, A

Prasad, M

Lin, C-T

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Sensors (Basel, Switzerland), 2021, 21, (18), pp. 6104-6104
Issue Date:: 2021-09-12

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Field	Value	Language
dc.contributor.author	Elgharabawy, A https://orcid.org/0000-0003-3381-4708
dc.contributor.author	Prasad, M https://orcid.org/0000-0001-9719-2253
dc.contributor.author	Lin, C-T
dc.date.accessioned	2021-12-16T04:24:53Z
dc.date.available	2021-09-10
dc.date.available	2021-12-16T04:24:53Z
dc.date.issued	2021-09-12
dc.identifier.citation	Sensors (Basel, Switzerland), 2021, 21, (18), pp. 6104-6104
dc.identifier.issn	1424-8220
dc.identifier.issn	1424-8220
dc.identifier.uri	http://hdl.handle.net/10453/152360
dc.description.abstract	Subgroup label ranking aims to rank groups of labels using a single ranking model, is a new problem faced in preference learning. This paper introduces the Subgroup Preference Neural Network (<i>SGPNN</i>) that combines multiple networks have different activation function, learning rate, and output layer into one artificial neural network (<i>ANN</i>) to discover the hidden relation between the subgroups' multi-labels. The <i>SGPNN</i> is a feedforward (<i>FF</i>), partially connected network that has a single middle layer and uses stairstep (<i>SS</i>) multi-valued activation function to enhance the prediction's probability and accelerate the ranking convergence. The novel structure of the proposed <i>SGPNN</i> consists of a multi-activation function neuron (<i>MAFN</i>) in the middle layer to rank each subgroup independently. The <i>SGPNN</i> uses gradient ascent to maximize the Spearman ranking correlation between the groups of labels. Each label is represented by an output neuron that has a single <i>SS</i> function. The proposed <i>SGPNN</i> using conjoint dataset outperforms the other label ranking methods which uses each dataset individually. The proposed <i>SGPNN</i> achieves an average accuracy of 91.4% using the conjoint dataset compared to supervised clustering, decision tree, multilayer perceptron label ranking and label ranking forests that achieve an average accuracy of 60%, 84.8%, 69.2% and 73%, respectively, using the individual dataset.
dc.format	Electronic
dc.language	eng
dc.publisher	MDPI AG
dc.relation	http://purl.org/au-research/grants/arc/DP180100656
dc.relation	http://purl.org/au-research/grants/arc/DP210101093
dc.relation.ispartof	Sensors (Basel, Switzerland)
dc.relation.isbasedon	10.3390/s21186104
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0301 Analytical Chemistry, 0502 Environmental Science and Management, 0602 Ecology, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Analytical Chemistry
dc.subject.mesh	Neurons
dc.subject.mesh	Cluster Analysis
dc.subject.mesh	Learning
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Cluster Analysis
dc.subject.mesh	Learning
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Neurons
dc.title	Subgroup Preference Neural Network.
dc.type	Journal Article
utslib.citation.volume	21
utslib.location.activity	Switzerland
utslib.for	0301 Analytical Chemistry
utslib.for	0502 Environmental Science and Management
utslib.for	0602 Ecology
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2021-12-16T04:24:47Z
pubs.issue	18
pubs.publication-status	Published
pubs.volume	21
utslib.citation.issue	18

Abstract:

Subgroup label ranking aims to rank groups of labels using a single ranking model, is a new problem faced in preference learning. This paper introduces the Subgroup Preference Neural Network (SGPNN) that combines multiple networks have different activation function, learning rate, and output layer into one artificial neural network (ANN) to discover the hidden relation between the subgroups' multi-labels. The SGPNN is a feedforward (FF), partially connected network that has a single middle layer and uses stairstep (SS) multi-valued activation function to enhance the prediction's probability and accelerate the ranking convergence. The novel structure of the proposed SGPNN consists of a multi-activation function neuron (MAFN) in the middle layer to rank each subgroup independently. The SGPNN uses gradient ascent to maximize the Spearman ranking correlation between the groups of labels. Each label is represented by an output neuron that has a single SS function. The proposed SGPNN using conjoint dataset outperforms the other label ranking methods which uses each dataset individually. The proposed SGPNN achieves an average accuracy of 91.4% using the conjoint dataset compared to supervised clustering, decision tree, multilayer perceptron label ranking and label ranking forests that achieve an average accuracy of 60%, 84.8%, 69.2% and 73%, respectively, using the individual dataset.

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http://hdl.handle.net/10453/152360