Name
Abstract | ||
|---|---|---|
In this paper the k-nearest-neighbours (KNN) based method is presented for the classification of time series which use qualitative learning to identify similarities using kernels. To this end, time series are transformed into symbol strings by means of several discretization methods and a distance based on a kernel between symbols in ordinal scale is used to calculate the similarity between time series. Hence, the idea proposed is the consideration of the simultaneous use of symbolic representation together with a kernel based approach for classification of time series. The methodology has been tested and compared with quantitative learning from a television-viewing shared data set and has yielded a high success identification ratio. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.eswa.2009.01.066 | Expert Syst. Appl. |
Keywords | Field | DocType |
discretization method,symbolic representation,time series,ordinal scale,discretization,quantitative learning,kernel,high success identification ratio,symbol string,new approach,similarity,simultaneous use,k -nearest-neighbours | Kernel (linear algebra),Discretization,Pattern recognition,Ordinal Scale,Symbol,Computer science,Artificial intelligence,String kernel,Machine learning | Journal |
Volume | Issue | ISSN |
36 | 6 | Expert Systems With Applications |
Citations | PageRank | References |
5 | 0.45 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| L. Gonzalez-Abril | 1 | 153 | 8.48 |
| F. Velasco | 2 | 106 | 5.83 |
| J. A. Ortega | 3 | 99 | 7.03 |
| F. J. Cuberos | 4 | 40 | 1.83 |