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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Piramuthu, S. |
| Copyright Year | 1998 |
| Description | Author affiliation: Decision & Inf. Sci., Florida Univ., Gainesville, FL, USA (Piramuthu, S.) |
| Abstract | Recent advances in computing technology in terms of speed, cost, as well as access to tremendous amounts of computing power and the ability to process huge amounts of data in reasonable time have spurred increased interest in data mining applications. Machine learning has been one of the methods used in most of these data mining applications. The data used as input to any of these learning systems are the primary source of knowledge in terms of what is learned by these systems. There have been relatively few studies on preprocessing data used as input in these data mining systems. In this study, we evaluate several feature selection methods as to their effectiveness in preprocessing input data. We use real-world financial credit-risk data in evaluating these systems. |
| Starting Page | 294 |
| Ending Page | 301 |
| File Size | 849025 |
| Page Count | 8 |
| File Format | |
| ISBN | 0818682558 |
| DOI | 10.1109/HICSS.1998.648324 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1998-01-09 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Data mining Learning systems Data preprocessing Neural networks Computers Costs Spatial databases Statistical analysis Decision trees Machine learning |
| Content Type | Text |
| Resource Type | Article |
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