Name
Abstract | ||
|---|---|---|
Feature selection has long been an active research topic in machine learning. Beginning with an empty set of features, it selects features most necessary for learning a target concept. Feature elimination, a newer technique, starts out with a full set of features and eliminates those most unnecessary for learning the target concept. Feature elimination tends to be more effective, can capture interacting features more easily, and suffers less from feature interaction than feature selection. Because the most unnecessary features are eliminated from the beginning, they will not mislead the induction process in terms of efficiency or accuracy. Induction-algorithm-oriented feature elimination, with particular parameter configurations, can achieve higher predictive accuracy than existing popular feature selection approaches. We propose two sets of well-tuned parameters based on empirical analysis. To understand how to achieve the best performance possible from IAOFE, we conducted a comprehensive analysis of IAOFE parameter tuning. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/MIS.2004.1274910 | IEEE Intelligent Systems |
Keywords | Field | DocType |
bayes methods,induction-algorithm-oriented feature elimination,comparative study,feature elimination,empirical analysis,iaofe parameter tuning,learning by example,popular feature selection approach,parameter setting,parameter tuning,suggested parameter configuration,inductive learning.,various parameter setting,parameter configuration,target concept,feature selection,machine learning,article report,abundant parameter,feature interaction,empirical evidence | Data mining,Dimensionality reduction,Feature selection,Computer science,Feature (machine learning),Artificial intelligence,k-nearest neighbors algorithm,Feature vector,Pattern recognition,Feature (computer vision),Algorithm,Feature extraction,Feature learning | Journal |
Volume | Issue | ISSN |
19 | 2 | 1541-1672 |
Citations | PageRank | References |
2 | 0.36 | 15 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ying Yang | 1 | 206 | 10.51 |
| Xindong Wu | 2 | 8830 | 503.63 |