Rayner Alfred (2010) Feature transformation: A genetic-based feature construction method for data summarization. Computational Intelligence, 26 (3). pp. 337-357. ISSN 0824-7935
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Abstract
The importance of input representation has been recognized already in machine learning. This article discusses the application of genetic-based feature construction methods to generate input data for the data summarization method called Dynamic Aggregation of Relational Attributes (DARA). Here, feature construction methods are applied to improve the descriptive accuracy of the DARA algorithm. The DARA algorithm is designed to summarize data stored in the nontarget tables by clustering them into groups, where multiple records stored in nontarget tables correspond to a single record stored in a target table. This article addresses the question whether or not the descriptive accuracy of the DARA algorithm benefits from the feature construction process. This involves solving the problem of constructing a relevant set of features for the DARA algorithm by using a genetic-based algorithm. This work also evaluates several scoring measures used as fitness functions to find the best set of constructed features. © 2010 Wiley Periodicals, Inc.
| Item Type: | Article |
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| Keyword: | Clustering data; Data summarizations; Dynamic aggregation; Feature construction, Feature transformations, Fitness functions, Genetic-based algorithms, Input datas, Machine-learning, Scoring measures |
| Subjects: | Q Science > QA Mathematics > QA1-939 Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science |
| Department: | SCHOOL > School of Engineering and Information Technology |
| Depositing User: | ADMIN ADMIN |
| Date Deposited: | 02 Mar 2011 12:43 |
| Last Modified: | 20 Oct 2017 14:42 |
| URI: | https://eprints.ums.edu.my/id/eprint/1969 |
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