Name
Abstract | ||
|---|---|---|
The use of supervised learning techniques for fitting weights and/or generator functions of weighted quasi-arithmetic means - a special class of idempotent and nondecreasing aggregation functions - to empirical data has already been considered in a number of papers. Nevertheless, there are still some important issues that have not been discussed in the literature yet. In the second part of this two-part contribution we deal with a quite common situation in which we have inputs coming from different sources, describing a similar phenomenon, but which have not been properly normalized. In such a case, idempotent and nondecreasing functions cannot be used to aggregate them unless proper preprocessing is performed. The proposed idempotization method, based on the notion of B-splines, allows for an automatic calibration of independent variables. The introduced technique is applied in an R source code plagiarism detection system. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-40581-0_63 | Communications in Computer and Information Science |
Keywords | Field | DocType |
Aggregation functions,Weighted quasi-arithmetic means,Least squares fitting,Idempotence | Least squares,Discrete mathematics,Normalization (statistics),Plagiarism detection,Source code,Algorithm,Supervised learning,Variables,Operator (computer programming),Idempotence,Mathematics | Conference |
Volume | ISSN | Citations |
611 | 1865-0929 | 1 |
PageRank | References | Authors |
0.41 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maciej Bartoszuk | 1 | 17 | 3.62 |
| Gleb Beliakov | 2 | 989 | 78.95 |
| Marek Gagolewski | 3 | 186 | 23.86 |
| Simon James | 4 | 272 | 20.35 |