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Heuristic Search over a Ranking for Feature Selection
[chapter]
2005
Lecture Notes in Computer Science
In this work, we suggest a new feature selection technique that lets us use the wrapper approach for finding a well suited feature set for distinguishing experiment classes in high dimensional data sets. Our method is based on the relevance and redundancy idea, in the sense that a ranked-feature is chosen if additional information is gained by adding it. This heuristic leads to considerably better accuracy results, in comparison to the full set, and other representative feature selection
doi:10.1007/11494669_91
fatcat:j6rogwtq6bhzppcxttgcszhj2q