| _version_ |
1860799753374138368
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| building |
INTELEK Repository
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| collection |
Online Access
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| collectionurl |
https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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| date |
2019-09-19 01:25:28
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| eventvenue |
Bali, Indonesia
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| format |
Restricted Document
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| id |
7259
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| institution |
UniSZA
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| originalfilename |
2592-01-FH03-FIK-19-28012.pdf
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| person |
Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML
like Gecko) Chrome/76.0.3809.132 Safari/537.36
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| recordtype |
oai_dc
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| resourceurl |
https://intelek.unisza.edu.my/intelek/pages/view.php?ref=7259
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| spelling |
7259 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=7259 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Conference Conference Paper application/pdf 4 1.6 Adobe Acrobat Pro DC 20 Paper Capture Plug-in Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML like Gecko) Chrome/76.0.3809.132 Safari/537.36 2019-09-19 01:25:28 2592-01-FH03-FIK-19-28012.pdf UniSZA Private Access Complexity approximation of classification task for large dataset ensemble artificial neural networks In this paper, operational and complexity analysis model for ensemble Artificial Neural Networks (ANN) multiple classifiers are investigated. The main idea behind this, is lie on large dataset classification complexity and burden are to be moderated by using partitioning for parallel tasks and combining them to enhance the capability of a classifier. The complexity of the single ANN and ensemble ANN are obtained from the estimates of upper bounds of converged functional error with the partitioning of dataset. The estimates derived using Apriori method shows that the use of an ensemble ANN with different approach is feasible where such problem with a high number of inputs and classes can be solved with time complexity of O(n ) for some k, which is a type of polynomial. This result is in line with the importance of good performance achieved by diversity rule applied with the use of reordering technique. As a conclusion, an ensemble heterogeneous ANN classifier is practical and relevance to theoretical and experimental of combiners for ensemble ANN classifier systems for large dataset. 2nd International Conference on Advanced Data and Information Engineering, DaEng 2015 Bali, Indonesia
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| spellingShingle |
Complexity approximation of classification task for large dataset ensemble artificial neural networks
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| summary |
In this paper, operational and complexity analysis model for ensemble Artificial Neural Networks (ANN) multiple classifiers are investigated. The main idea behind this, is lie on large dataset classification complexity and burden are to be moderated by using partitioning for parallel tasks and combining them to enhance the capability of a classifier. The complexity of the single ANN and ensemble ANN are obtained from the estimates of upper bounds of converged functional error with the partitioning of dataset. The estimates derived using Apriori method shows that the use of an ensemble ANN with different approach is feasible where such problem with a high number of inputs and classes can be solved with time complexity of O(n ) for some k, which is a type of polynomial. This result is in line with the importance of good performance achieved by diversity rule applied with the use of reordering technique. As a conclusion, an ensemble heterogeneous ANN classifier is practical and relevance to theoretical and experimental of combiners for ensemble ANN classifier systems for large dataset.
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| title |
Complexity approximation of classification task for large dataset ensemble artificial neural networks
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| title_full |
Complexity approximation of classification task for large dataset ensemble artificial neural networks
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| title_fullStr |
Complexity approximation of classification task for large dataset ensemble artificial neural networks
|
| title_full_unstemmed |
Complexity approximation of classification task for large dataset ensemble artificial neural networks
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| title_short |
Complexity approximation of classification task for large dataset ensemble artificial neural networks
|
| title_sort |
complexity approximation of classification task for large dataset ensemble artificial neural networks
|