Name
Title | ||
|---|---|---|
A Genetic Algorithm for Process Discovery Guided by Completeness, Precision and Simplicity. |
Abstract | ||
|---|---|---|
Several process discovery algorithms have been presented in the last years. These approaches look for complete, precise and simple models. Nevertheless, none of the current proposals obtains a good integration between the three objectives and, therefore, the mined models have differences with the real models. In this paper we present a genetic algorithm (ProDiGen) with a hierarchical fitness function that takes into account completeness, precision and simplicity. Moreover, ProDiGen uses crossover and mutation operators that focus the search on those parts of the model that generate errors during the processing of the log. The proposal has been validated with 21 different logs. Furthermore, we have compared our approach with two of the state of the art algorithms. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-10172-9_8 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Process mining,process discovery,Petri nets,genetic mining | Crossover,Petri net,Computer science,Fitness function,Artificial intelligence,Business process discovery,Completeness (statistics),Machine learning,Genetic algorithm,Mutation operator,Process mining | Conference |
Volume | ISSN | Citations |
8659 | 0302-9743 | 1 |
PageRank | References | Authors |
0.36 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Borja Vázquez-Barreiros | 1 | 49 | 5.82 |
| Manuel Mucientes | 2 | 378 | 35.05 |
| Manuel Lama | 3 | 383 | 34.84 |