Name
Abstract | ||
|---|---|---|
This paper introduces a multiobjective grammar based genetic programming algorithm to solve a Web Mining problem from multiple instance perspective. This algorithm, called MOG3P-MI, is evaluated and compared with other available algorithms which extend a well-known neighborhood-based algorithm (k-nearest neighbour algorithm) and with a mono objective version of grammar guided genetic programming G3P-MI. Computational experiments show that, the MOG3PMI algorithm obtains the best results, solves problems of k-nearest neighbour algorithms, such as sparsity and scalability, adds comprehensibility and clarity in the knowledge discovery process and overcomes the results of monoobjective version. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/HIS.2008.120 | Barcelona |
Keywords | Field | DocType |
web mining problem,monoobjective version,amono objective version,well-known neighborhood-based algorithm,mog3pmi algorithm,genetic programming algorithm,available algorithm,multiple instance learning,multiobjective grammar,genetic programming g3p-mi,multiobjective genetic programming,k-nearest neighbour algorithm,classification algorithms,indexes,web pages,internet,web mining,learning artificial intelligence,accuracy,mil,sensitivity,computer experiment,genetic programming,data mining,algorithm design and analysis,genetic algorithms | Web mining,Algorithm design,Web page,Computer science,Genetic programming,Knowledge extraction,Artificial intelligence,Statistical classification,Population-based incremental learning,Genetic algorithm | Conference |
ISBN | Citations | PageRank |
978-0-7695-3326-1 | 1 | 0.37 |
References | Authors | |
22 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amelia Zafra | 1 | 432 | 22.64 |
| Eva Gibaja | 2 | 51 | 5.50 |
| S. Ventura | 3 | 2318 | 158.44 |