In feature selection, χ 2 measures the independence of features with respect to the class.
Assuming the independence of features, using a leave one out cross validation, the classification accuracy of s th feature and j th sample in training set with size J, and l∈J is found by the identification of the class as: w ∗ = arg min l, l ≠ j d ( z sj, z sl ) (9).
Since independence of features cannot be assumed a priori, we adjusted the selection procedure to consider the mutual correlation between features as well as their separation power.
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NB assumes conditional independence between features.
NB is based upon Bayes' theorem and assumes conditional independence between features.
It is this naive assumption of independence among features to which the term "Naive" in "Naive Bayesian" refers.
This is not an optimal representation for machine learning as some algorithms assume independence between features and this is not the case.
Assuming conditional independence among features given the match class, the framework estimates the probabilistic parameters of the record linkage model in an unsupervised fashion.
By assuming the independence of features, we can calculate p s ij → | h *, ω c as p s ij → | h *, ω c = ∑ m = 1 K h * i s α h * i, m s × G s ij → | μ h * i, m s, Σ h * i, m s, (20).
