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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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2012
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| Subjects: | |
| Online Access: | 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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| Institution: | Universiti Utara Malaysia |
| Language: | English |
| Summary: | 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. |
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