Name
Title | ||
|---|---|---|
Investigating The Influence Of Feature Correlations On Automatic Relevance Determination |
Abstract | ||
|---|---|---|
Feature selection is the technique commonly used in machine learning to select a subset of relevant features for building robust learning models. Ensemble feature relevance determination can properly group the most relevant features together and separate the relevant features from the irrelevant and redundant features. However, it cannot provide reliable local feature relevance rank. In this paper, we demonstrate that the predicted local relevance rank for the relevant features could be influenced by their highly correlated redundant features, according to the strength of their correlations. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/IJCNN.2008.4633865 | 2008 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-8 |
Keywords | Field | DocType |
machine learning,learning artificial intelligence,artificial neural networks,feature extraction,feature selection,neural networks,statistical analysis | Feature correlation,Feature selection,Pattern recognition,Computer science,Robust learning,Feature extraction,Artificial intelligence,Feature relevance,Artificial neural network,Machine learning,Statistical analysis | Conference |
ISSN | Citations | PageRank |
2161-4393 | 0 | 0.34 |
References | Authors | |
8 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Fu | 1 | 110 | 3.38 |
| Antony Browne | 2 | 129 | 9.39 |