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Correlation-based Feature Ordering for Classification based on Neural Incremental Attribute Learning
2012
International Journal of Machine Learning and Computing
Incremental Attribute Learning (IAL) is a novel supervised machine learning approach, which sequentially trains features one by one. Thus feature ordering is very important to IAL. Previous studies on feature ordering only concentrated on the contribution of each feature to different outputs. However, besides contribution, correlations among input features and output categories are also very important to the final classification result, which has not yet been researched in feature ordering but
doi:10.7763/ijmlc.2012.v2.242
fatcat:qxivv7rmvzg4vbcezr5zk2a5se