Tree-based mining contrast subspace
All existing mining contrast subspace methods employ density-based likelihood contrast scoring function to measure the likelihood of a query object to a target class against other class in a subspace. However, the density tends to decrease when the dimensionality of subspaces increases causes its bo...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Ahmad Dahlan
2019-07-01
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| Series: | IJAIN (International Journal of Advances in Intelligent Informatics) |
| Online Access: | http://ijain.org/index.php/IJAIN/article/view/359 |
| _version_ | 1818853218255372288 |
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| author | Florence Sia Rayner Alfred |
| author_facet | Florence Sia Rayner Alfred |
| author_sort | Florence Sia |
| collection | DOAJ |
| description | All existing mining contrast subspace methods employ density-based likelihood contrast scoring function to measure the likelihood of a query object to a target class against other class in a subspace. However, the density tends to decrease when the dimensionality of subspaces increases causes its bounds to identify inaccurate contrast subspaces for the given query object. This paper proposes a novel contrast subspace mining method that employs tree-based likelihood contrast scoring function which is not affected by the dimensionality of subspaces. The tree-based scoring measure recursively binary partitions the subspace space in the way that objects belong to the target class are grouped together and separated from objects belonging to other class. In contrast subspace, the query object should be in a group having a higher number of objects of the target class than other class. It incorporates the feature selection approach to find a subset of one-dimensional subspaces with high likelihood contrast score with respect to the query object. Therefore, the contrast subspaces are then searched through the selected subset of one-dimensional subspaces. An experiment is conducted to evaluate the effectiveness of the tree-based method in terms of classification accuracy. The experiment results show that the proposed method has higher classification accuracy and outperform the existing method on several real-world data sets. |
| first_indexed | 2024-12-19T07:33:19Z |
| format | Article |
| id | doaj.art-4b4674212be84488a3cbec4839804361 |
| institution | Directory Open Access Journal |
| issn | 2442-6571 2548-3161 |
| language | English |
| last_indexed | 2024-12-19T07:33:19Z |
| publishDate | 2019-07-01 |
| publisher | Universitas Ahmad Dahlan |
| record_format | Article |
| series | IJAIN (International Journal of Advances in Intelligent Informatics) |
| spelling | doaj.art-4b4674212be84488a3cbec48398043612022-12-21T20:30:38ZengUniversitas Ahmad DahlanIJAIN (International Journal of Advances in Intelligent Informatics)2442-65712548-31612019-07-015216917810.26555/ijain.v5i2.359118Tree-based mining contrast subspaceFlorence Sia0Rayner Alfred1Knowledge Technology Research Unit, Univeristi Malaysia SabahKnowledge Technology Research Unit, Univeristi Malaysia SabahAll existing mining contrast subspace methods employ density-based likelihood contrast scoring function to measure the likelihood of a query object to a target class against other class in a subspace. However, the density tends to decrease when the dimensionality of subspaces increases causes its bounds to identify inaccurate contrast subspaces for the given query object. This paper proposes a novel contrast subspace mining method that employs tree-based likelihood contrast scoring function which is not affected by the dimensionality of subspaces. The tree-based scoring measure recursively binary partitions the subspace space in the way that objects belong to the target class are grouped together and separated from objects belonging to other class. In contrast subspace, the query object should be in a group having a higher number of objects of the target class than other class. It incorporates the feature selection approach to find a subset of one-dimensional subspaces with high likelihood contrast score with respect to the query object. Therefore, the contrast subspaces are then searched through the selected subset of one-dimensional subspaces. An experiment is conducted to evaluate the effectiveness of the tree-based method in terms of classification accuracy. The experiment results show that the proposed method has higher classification accuracy and outperform the existing method on several real-world data sets.http://ijain.org/index.php/IJAIN/article/view/359 |
| spellingShingle | Florence Sia Rayner Alfred Tree-based mining contrast subspace IJAIN (International Journal of Advances in Intelligent Informatics) |
| title | Tree-based mining contrast subspace |
| title_full | Tree-based mining contrast subspace |
| title_fullStr | Tree-based mining contrast subspace |
| title_full_unstemmed | Tree-based mining contrast subspace |
| title_short | Tree-based mining contrast subspace |
| title_sort | tree based mining contrast subspace |
| url | http://ijain.org/index.php/IJAIN/article/view/359 |
| work_keys_str_mv | AT florencesia treebasedminingcontrastsubspace AT rayneralfred treebasedminingcontrastsubspace |