Property | Value |
Name | NCL-TR-2010008 |
Description |
XCS with Bit Mask in Data Mining
Abstract: In this paper, an adapted XCS is proposed to reduce the numbers of the rules. The XCS is a branch of learning classifier systems which has been proven finding accurate maximal generalizations and has good performance on difficult problems. However, it usually produces too much rules to lower readability of the classification model. That is, people may not be able to get the needed knowledge or useful information out of the model. To solve this problem, a new mutation called Bit mask is devised in order to reduce the number of classification rules and therefore to improve the readability of the generated prediction model. We did a series of N-multiplexer experiments, including 6-bit, 11-bit, and 20-bit multiplexers to examine the performance of the proposed method. For the integer inputs, two synthetic oblique datasets, "Random-Data2" and "Random-Data9" are used to compare the performance of XCS and the proposed method. Moreover, the real world data is also used in the experience. According to the experimental results, the proposed method is verified that it has the capacity to reduce the classification rules with high prediction accuracy on the test problems. |
Filename | NCL-TR-2010008.pdf |
Filesize | 891.99 kB |
Filetype | pdf (Mime Type: application/pdf) |
Creator | ypchen |
Created On: | 09/01/2010 17:17 |
Viewers | Everybody |
Maintained by | Editor |
Hits | 2670 Hits |
Last updated on | 12/09/2010 12:16 |
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