Classification of machine learning engines using latent semantic indexing
With the huge increase of software functionalities, sizes and application domain, the difficulty of categorizing and classifying software for information retrieval and maintenance purposes is on demand.This work includes the use of Latent Semantic Indexing (LSI) in classifying neural network and k-...
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my.uum.repo.109472015-05-25T03:23:14Z http://repo.uum.edu.my/10947/ Classification of machine learning engines using latent semantic indexing Yusof, Yuhanis Alhersh, Taha Mahmuddin, Massudi Mohamed Din, Aniza QA76 Computer software With the huge increase of software functionalities, sizes and application domain, the difficulty of categorizing and classifying software for information retrieval and maintenance purposes is on demand.This work includes the use of Latent Semantic Indexing (LSI) in classifying neural network and k-nearest neighborhood source code programs. Functional descriptors of each program are identified by extracting terms contained in the source code.In addition, information on where the terms are extracted from is also incorporated in the LSI.Based on the undertaken experiment, the LSI classifier is noted to generate a higher precision and recall compared to the C4.5 algorithm as provided in the Weka tool. 2012-07-04 Conference or Workshop Item PeerReviewed application/pdf en http://repo.uum.edu.my/10947/1/CR197%281%29.pdf Yusof, Yuhanis and Alhersh, Taha and Mahmuddin, Massudi and Mohamed Din, Aniza (2012) Classification of machine learning engines using latent semantic indexing. In: Knowledge Management International Conference (KMICe) 2012, 4 – 6 July 2012, Johor Bahru, Malaysia. http://www.kmice.uum.edu.my |
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QA76 Computer software Yusof, Yuhanis Alhersh, Taha Mahmuddin, Massudi Mohamed Din, Aniza Classification of machine learning engines using latent semantic indexing |
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With the huge increase of software functionalities, sizes and application domain, the difficulty of categorizing and classifying software for information
retrieval and maintenance purposes is on demand.This work includes the use of Latent Semantic Indexing (LSI) in classifying neural network and k-nearest neighborhood source code programs. Functional descriptors of each program are identified by extracting terms contained in the source code.In addition, information on where the terms are extracted from is also incorporated in the LSI.Based on the undertaken experiment, the LSI classifier is noted to generate a higher precision and
recall compared to the C4.5 algorithm as provided in the Weka tool. |
format |
Conference or Workshop Item |
author |
Yusof, Yuhanis Alhersh, Taha Mahmuddin, Massudi Mohamed Din, Aniza |
author_facet |
Yusof, Yuhanis Alhersh, Taha Mahmuddin, Massudi Mohamed Din, Aniza |
author_sort |
Yusof, Yuhanis |
title |
Classification of machine learning engines
using latent semantic indexing |
title_short |
Classification of machine learning engines
using latent semantic indexing |
title_full |
Classification of machine learning engines
using latent semantic indexing |
title_fullStr |
Classification of machine learning engines
using latent semantic indexing |
title_full_unstemmed |
Classification of machine learning engines
using latent semantic indexing |
title_sort |
classification of machine learning engines
using latent semantic indexing |
publishDate |
2012 |
url |
http://repo.uum.edu.my/10947/1/CR197%281%29.pdf http://repo.uum.edu.my/10947/ http://www.kmice.uum.edu.my |
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1644280504226152448 |
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13.211869 |