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Fuzzy output error as the performance function for training artificial neural networks to predict reading comprehension from eye gaze

Copeland, Leana; Gedeon, Tamas (Tom); Mendis, B Sumudu

Description

Imbalanced data sets are common in real life and can have a negative effect on classifier performance. We propose using fuzzy output error (FOE) as an alternative performance function to mean square error (MSE) for training feed forward neural networks to overcome this problem. The imbalanced data sets we use are eye gaze data recorded from reading and answering a tutorial and quiz. The goal is to predict the quiz scores for each tutorial page. We show that the use of FOE as the performance...[Show more]

dc.contributor.authorCopeland, Leana
dc.contributor.authorGedeon, Tamas (Tom)
dc.contributor.authorMendis, B Sumudu
dc.date.accessioned2015-12-10T23:13:42Z
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/1885/64544
dc.description.abstractImbalanced data sets are common in real life and can have a negative effect on classifier performance. We propose using fuzzy output error (FOE) as an alternative performance function to mean square error (MSE) for training feed forward neural networks to overcome this problem. The imbalanced data sets we use are eye gaze data recorded from reading and answering a tutorial and quiz. The goal is to predict the quiz scores for each tutorial page. We show that the use of FOE as the performance function for training neural networks provides significantly better classification of eye movements to reading comprehension scores. A neural network with three hidden layers of neurons gave the best classification results especially when FOE was used as the performance function for training. In these cases, upwards of a 19% reduction in misclassification was achieved compared to using MSE as the performance function.
dc.publisherSpringer
dc.rightsAuthor/s retain copyright
dc.sourceLecture Notes in Computer Science (LNCS)
dc.titleFuzzy output error as the performance function for training artificial neural networks to predict reading comprehension from eye gaze
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume8834
dc.date.issued2014
local.identifier.absfor080403 - Data Structures
local.identifier.absfor080602 - Computer-Human Interaction
local.identifier.ariespublicationa383154xPUB958
local.type.statusPublished Version
local.contributor.affiliationCopeland, Leana, College of Engineering and Computer Science, ANU
local.contributor.affiliationGedeon, Tamas (Tom), College of Engineering and Computer Science, ANU
local.contributor.affiliationMendis, B Sumudu, College of Engineering and Computer Science, ANU
local.bibliographicCitation.startpage586
local.bibliographicCitation.lastpage593
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciences
dc.date.updated2015-12-10T09:41:26Z
local.identifier.scopusID2-s2.0-84921475480
dcterms.accessRightsOpen Access
CollectionsANU Research Publications

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