Name
Abstract | ||
|---|---|---|
Dimensionality reduction and feature selection in particular are known to be of a great help for making supervised learning more effective and efficient. Many different feature selection techniques have been proposed for the traditional settings, where each instance is expected to have a label. In multiple instance learning (MIL) each example or bag consists of a variable set of instances, and the label is known for the bag as a whole, but not for the individual instances it consists of. Therefore, utilizing class labels for feature selection in MIL is not that straightforward and traditional approaches for feature selection are not directly applicable. This paper proposes a filter feature selection approach based on the ReliefF technique. It allows any previously designed MIL method to benefit from our feature selection approach, which helps to cope with the curse of dimensionality. Experimental results show the effectiveness of the proposed approach in MIL - different MIL algorithms tend to perform better when applied after the dimensionality reduction. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ISDA.2010.5687210 | Intelligent Systems Design and Applications |
Keywords | Field | DocType |
learning (artificial intelligence),pattern classification,ReliefF technique,dimensionality reduction approach,feature selection approach,multiple instance learning,supervised learning,Feature selection,Multiple instance learning | Kernel (linear algebra),Algorithm design,Dimensionality reduction,Feature selection,Pattern recognition,Computer science,Supervised learning,Curse of dimensionality,Artificial intelligence,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4244-8134-7 | 3 | 0.45 |
References | Authors | |
19 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amelia Zafra | 1 | 432 | 22.64 |
| Mykola Pechenizkiy | 2 | 1655 | 125.40 |
| S. Ventura | 3 | 2318 | 158.44 |