Name
Abstract | ||
|---|---|---|
This paper introduces a new Grammar-Guided Genetic Programming algorithm for resolving multi-instance learning problems. This algorithm, called G3P-MI, is evaluated and compared to other multi-instance classification techniques in different application domains. Computational experiments show that the G3P-MI often obtains consistently better results than other algorithms in terms of accuracy, sensitivity and specificity. Moreover, it makes the knowledge discovery process clearer and more comprehensible, by expressing information in the form of IF-THEN rules. Our results confirm that evolutionary algorithms are very appropriate for dealing with multi-instance learning problems. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.ins.2010.07.031 | Inf. Sci. |
Keywords | Field | DocType |
if-then rule,knowledge discovery process,better result,genetic programming algorithm,different application domain,evolutionary algorithm,new grammar-guided genetic programming,computational experiment,multiple instance learning,multi-instance classification technique,computer experiment | Stability (learning theory),Instance-based learning,Computer science,Algorithm,Genetic programming,Artificial intelligence,Genetic representation,Evolutionary programming,Population-based incremental learning,Machine learning,Weighted Majority Algorithm,Learning classifier system | Journal |
Volume | Issue | ISSN |
180 | 23 | 0020-0255 |
Citations | PageRank | References |
21 | 0.69 | 60 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amelia Zafra | 1 | 432 | 22.64 |
| S. Ventura | 2 | 2318 | 158.44 |