TY - GEN
T1 - Feature selection based on a new dependency measure
AU - Sha, Chaofeng
AU - Qiu, Xipeng
AU - Zhou, Aoying
PY - 2008
Y1 - 2008
N2 - Feature selection is a process commonly used in machine learning, wherein a subset of the features available from the data are selected for application of a learning algorithm. Feature selection is effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy and efficiency. In this paper, we propose a new information distance to measure the relevancy of two features. Unlike the information measure in previous feature selection works, our proposed information distance meets the condition of triangle inequality. We use InfoDist to feature selection and the experimental results showed it has a better performance.
AB - Feature selection is a process commonly used in machine learning, wherein a subset of the features available from the data are selected for application of a learning algorithm. Feature selection is effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy and efficiency. In this paper, we propose a new information distance to measure the relevancy of two features. Unlike the information measure in previous feature selection works, our proposed information distance meets the condition of triangle inequality. We use InfoDist to feature selection and the experimental results showed it has a better performance.
UR - https://www.scopus.com/pages/publications/58149117843
U2 - 10.1109/FSKD.2008.515
DO - 10.1109/FSKD.2008.515
M3 - 会议稿件
AN - SCOPUS:58149117843
SN - 9780769533056
T3 - Proceedings - 5th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2008
SP - 266
EP - 270
BT - Proceedings - 5th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2008
T2 - 5th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2008
Y2 - 18 October 2008 through 20 October 2008
ER -