A new classification technique based on hybrid fuzzy soft set theory and supervised fuzzy c-means
Recent advances in information technology have led to significant changes in today‟s world. The generating and collecting data have been increasing rapidly. Popular use of the World Wide Web (www) as a global information system led to a tremendous amount of information, and this can be in the...
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| Format: | Thesis |
| Language: | en en en |
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2013
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| Online Access: | http://eprints.uthm.edu.my/2198/1/24p%20BANA%20HANDAGA.pdf http://eprints.uthm.edu.my/2198/2/BANA%20HANDAGA%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/2198/3/BANA%20HANDAGA%20WATERMARK.pdf http://eprints.uthm.edu.my/2198/ |
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| author | Handaga, Bana |
| author_facet | Handaga, Bana |
| author_sort | Handaga, Bana |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Recent advances in information technology have led to significant changes in today‟s
world. The generating and collecting data have been increasing rapidly. Popular use
of the World Wide Web (www) as a global information system led to a tremendous
amount of information, and this can be in the form of text document. This explosive
growth has generated an urgent need for new techniques and automated tools that can
assist us in transforming the data into more useful information and knowledge. Data
mining was born for these requirements. One of the essential processes contained in
the data mining is classification, which can be used to classify such text documents
and utilize it in many daily useful applications. There are many classification
methods, such as Bayesian, K-Nearest Neighbor, Rocchio, SVM classifier, and Soft
Set Theory used to classify text document. Although those methods are quite
successful, but accuracy and efficiency are still outstanding for text classification
problem. This study is to propose a new approach on classification problem based on
hybrid fuzzy soft set theory and supervised fuzzy c-means. It is called Hybrid Fuzzy
Classifier (HFC). The HFC used the fuzzy soft set as data representation and then
using the supervised fuzzy c-mean as classifier. To evaluate the performance of
HFC, two well-known datasets are used i.e., 20 Newsgroups and Reuters-21578, and
compared it with the performance of classic fuzzy soft set classifiers and classic text
classifiers. The results show that the HFC outperforms up to 50.42% better as
compared to classic fuzzy soft set classifier and up to 0.50% better as compare
classic text classifier. |
| format | Thesis |
| id | my.uthm.eprints-2198 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en en en |
| publishDate | 2013 |
| record_format | eprints |
| spelling | my.uthm.eprints-21982021-10-31T04:01:25Z http://eprints.uthm.edu.my/2198/ A new classification technique based on hybrid fuzzy soft set theory and supervised fuzzy c-means Handaga, Bana QA Mathematics QA150-272.5 Algebra Recent advances in information technology have led to significant changes in today‟s world. The generating and collecting data have been increasing rapidly. Popular use of the World Wide Web (www) as a global information system led to a tremendous amount of information, and this can be in the form of text document. This explosive growth has generated an urgent need for new techniques and automated tools that can assist us in transforming the data into more useful information and knowledge. Data mining was born for these requirements. One of the essential processes contained in the data mining is classification, which can be used to classify such text documents and utilize it in many daily useful applications. There are many classification methods, such as Bayesian, K-Nearest Neighbor, Rocchio, SVM classifier, and Soft Set Theory used to classify text document. Although those methods are quite successful, but accuracy and efficiency are still outstanding for text classification problem. This study is to propose a new approach on classification problem based on hybrid fuzzy soft set theory and supervised fuzzy c-means. It is called Hybrid Fuzzy Classifier (HFC). The HFC used the fuzzy soft set as data representation and then using the supervised fuzzy c-mean as classifier. To evaluate the performance of HFC, two well-known datasets are used i.e., 20 Newsgroups and Reuters-21578, and compared it with the performance of classic fuzzy soft set classifiers and classic text classifiers. The results show that the HFC outperforms up to 50.42% better as compared to classic fuzzy soft set classifier and up to 0.50% better as compare classic text classifier. 2013-08 Thesis NonPeerReviewed text en http://eprints.uthm.edu.my/2198/1/24p%20BANA%20HANDAGA.pdf text en http://eprints.uthm.edu.my/2198/2/BANA%20HANDAGA%20COPYRIGHT%20DECLARATION.pdf text en http://eprints.uthm.edu.my/2198/3/BANA%20HANDAGA%20WATERMARK.pdf Handaga, Bana (2013) A new classification technique based on hybrid fuzzy soft set theory and supervised fuzzy c-means. Doctoral thesis, Universiti Tun Hussein Onn Malaysia. |
| spellingShingle | QA Mathematics QA150-272.5 Algebra Handaga, Bana A new classification technique based on hybrid fuzzy soft set theory and supervised fuzzy c-means |
| title | A new classification technique based on hybrid fuzzy soft set theory and supervised fuzzy c-means |
| title_full | A new classification technique based on hybrid fuzzy soft set theory and supervised fuzzy c-means |
| title_fullStr | A new classification technique based on hybrid fuzzy soft set theory and supervised fuzzy c-means |
| title_full_unstemmed | A new classification technique based on hybrid fuzzy soft set theory and supervised fuzzy c-means |
| title_short | A new classification technique based on hybrid fuzzy soft set theory and supervised fuzzy c-means |
| title_sort | new classification technique based on hybrid fuzzy soft set theory and supervised fuzzy c-means |
| topic | QA Mathematics QA150-272.5 Algebra |
| url | http://eprints.uthm.edu.my/2198/1/24p%20BANA%20HANDAGA.pdf http://eprints.uthm.edu.my/2198/2/BANA%20HANDAGA%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/2198/3/BANA%20HANDAGA%20WATERMARK.pdf http://eprints.uthm.edu.my/2198/ |
| url_provider | http://eprints.uthm.edu.my/ |