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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ashlock, D. Warner, E. |
| Copyright Year | 2008 |
| Description | Author affiliation: Dept. of Math. & Stat., Univ. of Guelph, Guelph, ON (Ashlock, D.; Warner, E.) |
| Abstract | Finite state machines are routinely used to efficiently recognize patterns in strings. The internal state structure of the machine is typically only of peripheral interest, appearing in algorithms only when the number of states is minimized in the interests of efficiency of execution or comparison. A side effect machine saves information about the internal transitions of the state machine. This record of internal state transitions forms an induced feature set for any string run through the side effect machine. In this study the number of times a machine passes though each state is used as a numerical feature set for classification. Finite state machines are trained with an evolutionary algorithm to produce feature sets that are very easy for an unsupervised learning algorithm, k-means clustering, to learn. The system is demonstrated on synthetic and biological data. The biological data are PCR-primers classified by their success at amplification. The parameters, number of states, population size, and mutation rates are explored to characterize their effect on performance. Side effect machines are found to be effective at recognizing classes of DNA sequence data. |
| Starting Page | 22 |
| Ending Page | 29 |
| File Size | 564719 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424417780 |
| DOI | 10.1109/CIBCB.2008.4675755 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-09-15 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | DNA Sequences Automata Clustering algorithms Machine learning Evolution (biology) Classification algorithms Testing Evolutionary computation Chaos |
| Content Type | Text |
| Resource Type | Article |
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