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We show that type III-L features are related to sudden changes in the radio emission observed at metric and decametric wavelengths, in particular the onset of new emitting sources at positions that can be at large distances from the flare site.
It is based on the simplistic assumption that the L features (x_{l}) are statistically independent given (omega _{j}).
Two extra considerations have to be taken into account for the projection of the X L and Y L features.
This bias increases with the value of L because the larger the value of L is the higher the chance of the L features becoming correlated.
Supposing there are L features selected by IHFST, described in Section 3.2, the new distance evaluation criteria β j of these selected features are also kept and act as feature weights of the selected features.
The SNB model performed best when (L_{s}) was equal 5,131 (that is, when the top 22% of the ranked L features were employed); the MCC value obtained was 0.786.
Formally, a pattern classification problem deals with the optimal assignment of an object to one of (J) predefined classes/categories, ((omega _1,omega _2,ldots,omega _J)), whereby it is assumed that the object is adequately (or even better, uniquely) characterized by L features x(_{l}), with (l=1,2,ldots,L).
Direct computation of (p(mathbf{x}|omega _{j})) from the training data set is not so straightforward as estimating (p omega _{j})) [5, 11 15], especially for L ≥ 100, as modeling a joint probability distribution that captures the relationship among the L features (x_{l}) given (omega _{j}) can become quite involved.
The l features corresponding to the l best values of the specific criterion are selected to form the best subset.
At the end of such a stability period a fluctuation occurs, the environment changes completely, that is each one of the L features composing the environment is complemented.
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