Name
Title | ||
|---|---|---|
Stochastic Semantic-Based Multi-objective Genetic Programming Optimisation for Classification of Imbalanced Data. |
Abstract | ||
|---|---|---|
Data sets with imbalanced class distribution pose serious challenges to well-established classifiers. In this work, we propose a stochastic multi-objective genetic programming based on semantics. We tested this approach on imbalanced binary classification data sets, where the proposed approach is able to achieve, in some cases, higher recall, precision and F-measure values on the minority class compared to C4.5, Naive Bayes and Support Vector Machine, without significantly decreasing these values on the majority class. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-62428-0_22 | ADVANCES IN SOFT COMPUTING, MICAI 2016, PT II |
Field | DocType | Volume |
Multi objective genetic programming,Data set,Pattern recognition,Naive Bayes classifier,Binary classification,Computer science,Support vector machine,Genetic programming,Artificial intelligence,Recall,Machine learning,Semantics | Conference | 10062 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Edgar Galván López | 1 | 81 | 8.87 |
| Lucia Vázquez-Mendoza | 2 | 0 | 0.68 |
| Leonardo Trujillo | 3 | 41 | 11.33 |