Efficient feature selection based on correlation measure between continuous and discrete features
To the best of our knowledge, few researches aim to measure the correlation between a continuous and a discrete feature. A theoretical framework is given to analyze properties of correlation measure between a continuous and a discrete feature. ECMBF selects feature subset by removing irrelevant features and redundant features. ECMBF can achieve high degree of dimensionality reduction and decrease classification error rates.
feature selection correlation measure discrete feature design of algorithms continuous feature mixture of continuous and discrete features