k-nearest neighbour using ensemble clustering based on feature selection approach to learning relational data
Due to the growing amount of data generated and stored in relational databases, relational learning has attracted the interest of researchers in recent years.Many approaches have been developed in order to learn relational data.One of the approaches used to learn relational data is Dynamic Aggregati...
| Κύριοι συγγραφείς: | , , , , , |
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| Μορφή: | Book Section |
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Springer International Publishing
2016
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| Θέματα: |
| _version_ | 1825804377764397056 |
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| author | Alfred, Rayner Shin, Kung Ke Sainin, Mohd Shamrie On, Chin Kim Pandiyan, Paulraj Murugesa Ag Ibrahim, Ag Asri |
| author2 | Akagi, Masato |
| author_facet | Akagi, Masato Alfred, Rayner Shin, Kung Ke Sainin, Mohd Shamrie On, Chin Kim Pandiyan, Paulraj Murugesa Ag Ibrahim, Ag Asri |
| author_sort | Alfred, Rayner |
| collection | UUM |
| description | Due to the growing amount of data generated and stored in relational databases, relational learning has attracted the interest of researchers in recent years.Many approaches have been developed in order to learn relational data.One of the approaches used to learn relational data is Dynamic Aggregation of Relational Attributes (DARA).The DARA algorithm is designed to summarize relational data with one-to-many relations. However, DARA suffers a major drawback when the cardinalities of attributes are very high because the size of the vector space representation depends on the number of unique values that exist for all attributes in the dataset.A feature selection process can be introduced to overcome this problem.These selected features can be further optimized to achieve a good classification result.Several clustering runs can be performed for different values of k to yield an ensemble of clustering results. This paper proposes a two-layered genetic algorithm-based feature selection in order to improve the classification performance of learning relational database using a k-NN ensemble classifier.The proposed method involves the task of omitting less relevant features but retaining the diversity of the classifiers so as to improve the performance of the k-NN ensemble. The result shows that the proposed k-NN ensemble is able to improve the performance of traditional k-NN classifiers. |
| first_indexed | 2024-07-04T06:13:33Z |
| format | Book Section |
| id | uum-20483 |
| institution | Universiti Utara Malaysia |
| last_indexed | 2024-07-04T06:13:33Z |
| publishDate | 2016 |
| publisher | Springer International Publishing |
| record_format | eprints |
| spelling | uum-204832017-01-02T08:37:24Z https://repo.uum.edu.my/id/eprint/20483/ k-nearest neighbour using ensemble clustering based on feature selection approach to learning relational data Alfred, Rayner Shin, Kung Ke Sainin, Mohd Shamrie On, Chin Kim Pandiyan, Paulraj Murugesa Ag Ibrahim, Ag Asri QA75 Electronic computers. Computer science Due to the growing amount of data generated and stored in relational databases, relational learning has attracted the interest of researchers in recent years.Many approaches have been developed in order to learn relational data.One of the approaches used to learn relational data is Dynamic Aggregation of Relational Attributes (DARA).The DARA algorithm is designed to summarize relational data with one-to-many relations. However, DARA suffers a major drawback when the cardinalities of attributes are very high because the size of the vector space representation depends on the number of unique values that exist for all attributes in the dataset.A feature selection process can be introduced to overcome this problem.These selected features can be further optimized to achieve a good classification result.Several clustering runs can be performed for different values of k to yield an ensemble of clustering results. This paper proposes a two-layered genetic algorithm-based feature selection in order to improve the classification performance of learning relational database using a k-NN ensemble classifier.The proposed method involves the task of omitting less relevant features but retaining the diversity of the classifiers so as to improve the performance of the k-NN ensemble. The result shows that the proposed k-NN ensemble is able to improve the performance of traditional k-NN classifiers. Springer International Publishing Akagi, Masato Nguyen, Thanh-Thuy Vu, Duc-Thai Phung, Trung-Nghia Huynh, Van-Nam 2016 Book Section PeerReviewed Alfred, Rayner and Shin, Kung Ke and Sainin, Mohd Shamrie and On, Chin Kim and Pandiyan, Paulraj Murugesa and Ag Ibrahim, Ag Asri (2016) k-nearest neighbour using ensemble clustering based on feature selection approach to learning relational data. In: Advances in Information and Communication Technology. Springer International Publishing, pp. 322-331. ISBN 978-3-319-49072-4 http://doi.org/10.1007/978-3-319-49073-1_35 doi:10.1007/978-3-319-49073-1_35 doi:10.1007/978-3-319-49073-1_35 |
| spellingShingle | QA75 Electronic computers. Computer science Alfred, Rayner Shin, Kung Ke Sainin, Mohd Shamrie On, Chin Kim Pandiyan, Paulraj Murugesa Ag Ibrahim, Ag Asri k-nearest neighbour using ensemble clustering based on feature selection approach to learning relational data |
| title | k-nearest neighbour using ensemble clustering based on feature selection approach to learning relational data |
| title_full | k-nearest neighbour using ensemble clustering based on feature selection approach to learning relational data |
| title_fullStr | k-nearest neighbour using ensemble clustering based on feature selection approach to learning relational data |
| title_full_unstemmed | k-nearest neighbour using ensemble clustering based on feature selection approach to learning relational data |
| title_short | k-nearest neighbour using ensemble clustering based on feature selection approach to learning relational data |
| title_sort | k nearest neighbour using ensemble clustering based on feature selection approach to learning relational data |
| topic | QA75 Electronic computers. Computer science |
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